Interpreting Sea Turtle Trophic Ecology through Stable Isotope Analysis

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Title:
Interpreting Sea Turtle Trophic Ecology through Stable Isotope Analysis
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1 online resource (155 p.)
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english
Creator:
Vander Zanden, Hannah
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University of Florida
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Gainesville, Fla.
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Degree:
Doctorate ( Ph.D.)
Degree Grantor:
University of Florida
Degree Disciplines:
Zoology, Biology
Committee Chair:
Bjorndal, Karen
Committee Members:
Bolten, Alan B
Palmer, Todd
Inglett, Patrick
Brenner, Mark

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Subjects / Keywords:
adult -- consistency -- diet -- discrimination -- green -- habitat -- isotope -- juvenile -- loggerhead -- sea -- specialization -- stable -- turtle
Biology -- Dissertations, Academic -- UF
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Zoology thesis, Ph.D.
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theses   ( marcgt )
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Abstract:
Endangered sea turtles spend most of their lives in marine habitats, where they are difficult to access and monitor.  As we confront current and future anthropogenic threats to sea turtle populations, it is critical to understand the resources and foraging areas used by these animals in developing management plans.  My work has focused on the ecology of green turtle and loggerhead foraging aided by the tool of stable isotope analysis.  Naturally occurring stable isotopes of carbon and nitrogen in tissue samples act as indicators to trace the diet and habitat used by individuals prior to the sampling period. First, I examined potential effects of individual variation on these isotope values as well as the offset in stable isotope ratios between four green turtle tissues and the diet.  Individual variation was quite small, and discrimination factors differed with life stage,and these measurements can help to better interpret stable isotope data from wild populations.  Next, I examined the isotopic patterns in green turtles at nesting and foraging areas to assess feeding patterns and population connectivity in Caribbean turtles.  I found that the stable isotope values of individuals are highly influenced by the location in which they forage, and these differences can aid in determining the proportion of turtles in a nesting population that originated from a particular foraging area. I also examined the long-term patterns in loggerhead and green turtle resource use through the chronological records that are contained in scute (the keratin layer of a turtle’s shell).  Both adult loggerheads and green turtles demonstrated long-term individual consistency in stable isotope values that is indicative of high fidelity to foraging grounds.  The temporal consistency and degree of specialization varied with life stage in green turtles but can help to identify the ecological roles and habitat specificity for these species. Together, this research aids in understanding the trophic ecology of these species. Information on where and what sea turtles eat is critical to protecting the areas they use most and for assessing the risk of encountering anthropogenic threats such incidental capture in fisheries or oil spills.
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In the series University of Florida Digital Collections.
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Includes vita.
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by Hannah Vander Zanden.
Thesis:
Thesis (Ph.D.)--University of Florida, 2012.
Local:
Adviser: Bjorndal, Karen.
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RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2014-08-31

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1 INTERPRETING SEA TURTLE TROPHIC ECOLOGY THROUGH STABLE ISOTOPE ANALYSIS By HANNAH B. VANDER ZANDEN A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS F OR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2012

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2 2012 Hannah B. Vander Zanden

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3 To Luis

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4 ACKNOWLEDGMENTS Though this dissertation bears the authorship line of a single individual, there are numerous people who have contri buted enormously and made my work possible First, I would like to thank my advisor and dissertation chair, Karen Bjorndal, who has spent the last six years teaching me not to hide the light under my bushel basket and plowing through more manuscript draft s than humanly possible Alan Bolten has ta ught me the art of preparedness, and I thank him for his dedication to the logistical support of my work through the years (no one else has the same knack with permits as he does) I thank all of my committee me mbers Karen Bjorndal, Alan Bolten, Mark Brenner, Pa trick Inglett, and Todd Palmer who have been exceptionally encouraging and supportive of my research. There are a number of individuals within the Department of Biology at the University of Florida who hav e contributed to my academic development and to whom I am graciously indebted First, I thank Kim Reich for steering me to stable isotope ecology and kindly sharing her time and knowledge on every aspect of the process from sample collection to data inter pretation. T he members of the Archie Carr Center for Sea Turtle Research: Peter Eliazar, Gabby Hrychyshyn, Melania Lpez Castro, Mariela Pajuelo, Joe Pfaller, Kim Reich, Alison Roark, Luciano Soares e Soares, Natalie Williams, and Patricia Zrate have con tributed to providing a supportive team and I tha nk Jamie Gillooly and members of his lab group including Andrew Hein and April Hayward for providing me a new scientific perspective and thoughtful conversations. I thank Ben Bolker J ake Ferguson, and Jose Miguel Ponciano for statistical help and helping me to overcome my fear of R. Several undergraduates have helped with multiple projects and sometimes tedious tasks,

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5 including: Joslyn Armstrong, Sophia Caccitore, Alice Chow, Nicole F rankel, Temma Kaufman. My office mates Christine Angelini, James Nifong, and Schuyler van Montfrans have also provided encouragement and a friendly working environment I thank the Department of Biology staff over the past six years for a slew of logistic s : Ken Albergotti, Diana Davis, Amy Dechow, Kathy Jones, Cathy Moore, Mike Gunter, Susan Hart, Johnna Lechler, Leila Long, Cindi Marsh, Vitrell McNair, Tangelyn Mitchell, Karen Patterson and Pete Ryschkewitsch Various individuals from several research s ites have contributed to making each project possible. Jason Curtis at the UF Light Stable Isotope Laboratory has been enormously helpful and generous and has efficiently analyzed the hundreds of turtle samples that make up this research. At the Cayman T urtle Farm, Walter Mustin provided all of the samples from gr een turtles. In the Bahamas, Randolph Burrows, S teve Connett, B arbara Crouchley, H enry Nixon and the Bahamas National Trust assisted with turtle sampling. Cathi Campbell and Cynthia Lagueux we re critical in sampling green turtles from Nicara gua along with William McCoy, Kevin Clark, and Jenny Clark. I thank the Sea Turtle Conservancy, and particularly Emma Harrison, plus the staff and research assistants at the John H. Phipps Biological Field Station in Costa Rica for field logistics in Tortuguero. I thank David Jones and Sarah Durose and other volunteers of Global Vision International for allowing me to participate in jaguar walks to collect scute samples from dead turtles at Tortuguero Ni ck Osman and J oh n Steiner assisted with sampling loggerheads at Canaveral National Seashore. Karen Arthur and Brian Popp from the University of Hawaii collaborated on the compound specific stable isotope analysis and provided extensive feedback on drafts of the manuscript. Other

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6 individuals also provided helpful reviews for drafts of chapters in th is dissertation, including Steff en Oppel, Carlos Martnez del Rio, and Jeff Seminoff. All samples were collected under the University of Florida Institution on Animal Care and Use Committee Protocol numbers 201101694 and 201101985. Additionally loggerhead scute samples were collected with Florida Fish and Wildlife Conservation Commission Marine Turtle Permit #016 and U.S. Department of the Interior National Par k Servi c e Permit # CANA 2004 SCI 0003 S amples were collected and processed in compliance with the Ministerio del Ambiente y los Recursos Naturales (MARENA) permits to Wildlife Conservation Society in Nicaragua and Ministerio del Ambiente y Energa (MINAE) permits to the Sea Turtle Conservancy (formerly Caribbean Conservation Corporation) in Costa Rica. Samples collected outside the U.S. were imported under CITES permit 11US72450/9. Funding for my dissertation work was provided by a National Science Founda tion Graduate Research Fellowship Sigma Xi Grant in Aid of Research, PADI Foundation G rant University of Florida Graduate Student Council Research Award, and the Department of Biology John Paul Olowo Memorial Research Grant. Additional funds were provid ed by the Lockhar t Dissertation F ellowship from the University of Florida Association for Academic Women and the University of Florida Howard Hughes Medical Institute Science for Life Graduate Student Award. Travel grants were provided from the University of Florida Graduate Student Council, Department of Biology, College of Liberal Arts and Sciences, and the International Sea Turtle Society. Funds for portions of my research were provided to Karen Bjorndal and Alan Bolten by the Disney Worldwide Conserva tion Fund Florida Sea Turtle Grant Program Knight Vision

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7 Foundation National Fish and Wildlife Foundation, U.S. National Marine Fisheries Service, U.S. Fish and Wildlife Service and the Sea Turtle Grans Program that is funded from proceeds from the sale of the Florida Sea Turtle License Plate I thank Rebecca Darnell, Bret Pasch, Kate Pasch, and Kathleen Rudolph for their friendship and academic support through the years W ithout Lee Anne Eareckson who ich I participated 15 years ago I may have ta ken a completely different path, as o bserving a sea turtle nest for the first time wa s a life changing experience. I thank my family for their love a nd encouragement throughout my graduate years and for their belief in the value of education. Finally I thank my partner, Luis lvarez Castro for his understanding, rational guidance, and unwavering support over the past five years.

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8 TABLE OF CONTENTS page ACKNOWLEDGMENTS ................................ ................................ ................................ .. 4 LIST OF TABLES ................................ ................................ ................................ .......... 10 LIST OF FIGURES ................................ ................................ ................................ ........ 11 LIST OF ABBREVIATIONS ................................ ................................ ........................... 13 ABSTRACT ................................ ................................ ................................ ................... 15 CHAPTER 1 BACKGROUND OF SEA TURTLE BIOLOGY AND STABLE ISOTOPE ECOLOGY ................................ ................................ ................................ .............. 17 Sea Turtle Life History ................................ ................................ ............................ 17 Stable Isotope Ecology ................................ ................................ ........................... 18 Applying Stable Isotope Analysis to Sea Turtle Ecology ................................ ......... 21 2 INHERENT VARIATION IN STABLE ISOTOPE VALUES AND DISCRIMINATION FACTORS IN TWO LIFE STAGES OF GREEN TURTLES ..... 26 Introduction ................................ ................................ ................................ ............. 26 Materials and Methods ................................ ................................ ............................ 28 Study Conditions ................................ ................................ .............................. 28 Sample Collection ................................ ................................ ............................ 29 Sample Preparation and Isotope Analysis ................................ ........................ 30 Data Analysis ................................ ................................ ................................ ... 31 Results ................................ ................................ ................................ .................... 34 Discussion ................................ ................................ ................................ .............. 37 Inherent Variation ................................ ................................ ............................. 37 Discrimination Factors ................................ ................................ ...................... 38 Outcomes ................................ ................................ ................................ ......... 43 3 TROPHIC ECOLOGY OF A GREEN TURTLE BREEDING POPULATION ........... 53 Introduction ................................ ................................ ................................ ............. 53 Materials and Methods ................................ ................................ ............................ 57 Sample Collection and Preparation ................................ ................................ .. 57 Sample Analyses ................................ ................................ .............................. 59 Turtle Trophic Position ................................ ................................ ...................... 61 Data Analysis ................................ ................................ ................................ ... 63 Results ................................ ................................ ................................ .................... 63 Discussion ................................ ................................ ................................ .............. 65 Interpreting the Isotopic Niche ................................ ................................ .......... 65

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9 Assessing Population Connectivity with the Isotopic Niche .............................. 67 Outcomes ................................ ................................ ................................ ......... 69 4 INDIVIDUAL SPECIALISTS IN A GENERALIST POPULATION: RESULTS FROM A LONG TERM STABLE ISOTOPE SERIES ................................ .............. 80 Introduction ................................ ................................ ................................ ............. 80 Materials and Methods ................................ ................................ ............................ 82 Scute Sampling and Analysis ................................ ................................ ........... 82 Estimation of Scute Age ................................ ................................ ................... 83 Results ................................ ................................ ................................ .................... 86 Discussion ................................ ................................ ................................ .............. 86 5 TEMPORAL CONSISTENCY AND INDIVIDUAL SPECIALIZATION IN RESOURCE USE BY GREEN TURTLES IN SUCCESSIVE LIFE STAGES .......... 96 Introduction ................................ ................................ ................................ ............. 96 Materials and Methods ................................ ................................ .......................... 100 Sample Collection ................................ ................................ .......................... 100 Sample Preparation and Analysis ................................ ................................ .. 101 Scute Growth Rate ................................ ................................ ......................... 103 Data Analysis ................................ ................................ ................................ 104 Results ................................ ................................ ................................ .................. 105 Scute Records ................................ ................................ ................................ 105 Temporal Consistency and Individual Speci alization ................................ ...... 107 Discussion ................................ ................................ ................................ ............ 107 Comparison Among Green Turtle Life Stages ................................ ................ 108 Scute Growth Rates ................................ ................................ ....................... 112 Outcomes ................................ ................................ ................................ ....... 113 6 CONCLUSIONS AND FURTHER RESEARCH ................................ .................... 126 Fundamentals ................................ ................................ ................................ ....... 126 Stable Isotopes Never Lie ................................ ................................ ..................... 1 27 Creatures of Habit ................................ ................................ ................................ 129 Onwards and Upwards ................................ ................................ ......................... 132 APPENDIX : GREEN TURTLE FEED INGREDIENTS ................................ ............. 138 LIST OF REFERENCES ................................ ................................ ............................. 139 BIOGRAPHICAL SKETCH ................................ ................................ .......................... 154

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10 LIST OF TABLES Table page 1 1 Abundance and standards used for stabl e isotopes used in this research ......... 25 2 1 BIC values for the ten models ................................ ................................ ............. 44 2 2 Mean 13 C and 15 ed for four tissues in two life stages from this study and other studies from the literature ................... 45 2 3 Pairwise comparisons among bivariate means ................................ ................... 46 2 4 Pairwise comparisons among bivariate variance covariance matrices ............... 47 2 5 13 15 N) measured in this study and for other sea turtle species reported from the literature. ................................ ................... 48 3 1 Number of green turtles, size range, and year sampled at each of the five foraging grounds and the nesting beach location ................................ ............... 70 3 2 Seagrass ( Thalassia testudinum ) carbon and nitrogen isotope compositions provided as mean and minimum/maximum values ................................ ............. 71 3 3 Mean and SE of 13 C and 15 N values of Thalassia testudium analyzed in this study and collected from the literature for sites in the Greater Caribbean ... 72 3 4 Bulk tissue and amino acid 15 N values of Tortuguero green turtle epidermis and seagrass ( Thalassia testudinum ) ................................ ................................ 73 4 1 Minimum, maximum, and mean ranges of 15 N and 13 C for individual scute records ................................ ................................ ................................ ............... 90 4 2 ANOVAs indicate significant differences between the means of individuals ....... 91 5 1 Scute samp les were collected from three life stages of green turtles at two locations ................................ ................................ ................................ ........... 115 5 2 Within individual contribution (WIC) and total niche width (TNW) approximat ed through the ANOVA framework amo ng three life stages ........... 116

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11 LIST OF FIGURES Figure page 2 1 Results of three models using the first parameterization in which mean and variance are est imated ................................ ................................ ....................... 49 2 2 Values of 13 C and 15 N and b ivariate 95% confidence ellipses for each tissue and life stage ................................ ................................ ............................ 51 2 3 Comparison of is otopic variation in epidermis samp les from juvenile green turtles ................................ ................................ ................................ ................. 52 3 1 Map of five foraging grounds and one nesting beach where green turtles were sampled. ................................ ................................ ................................ .... 75 3 2 Bulk tissue 13 C and 15 N values of green turtle s and seagrass ......................... 76 3 3 Difference in 15 N values between each amino acid and phenylalanine among primary produ cers ................................ ................................ ................... 77 3 4 Green turtle t rophic position ................................ ................................ ................ 78 3 5 The relationship between the bulk epidermis 15 N and phe 15 N values ............ 79 4 1 Conceptual model representing resource use through time .............................. 92 4 2 Values of 15 N and 13 C values in successive scute layers from 15 logge rheads ................................ ................................ ................................ ........ 93 4 3 Example of a shift in resource use ................................ ................................ ..... 94 4 4 Biplot of 13 C and 15 N loggerhead scute values ................................ ................ 95 5 1 Conceptual model of resource use ................................ ................................ ... 117 5 2 Stable isotope values in successive subsections of scute in a single neritic juvenile green turtle illustra ting a complete oceanic to neritic shift ................... 118 5 3 Stable isotope values in successive subsections of scute in 26 juvenile green turtles ................................ ................................ ................................ ................ 119 5 4 Stable isotope values in successive subsections of scute in 8 oceanic juvenile green turtles ................................ ................................ ........................ 121 5 5 Stable isotope values in successive subsections of scute in 14 neritic juvenile green turtles ................................ ................................ ................................ ...... 122

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12 5 6 Stable isotope values in successive subsections of scute in 21 adult green turtles ................................ ................................ ................................ ................ 123 5 7 Temporal consist ency and degree of individual specialization among life stages ................................ ................................ ................................ ............... 124 6 1 Characterization of three population types based on stable isotope ratios ....... 137

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13 LIST OF AB BREVIATIONS A A CSIA Compound specific stable isotope analysis of amino acids A NOVA Analysis of variance B IC Bayesian information criterion between individual component of variation C CL Curved carap a ce length C TF Cayman Turtle Farm D ERM Dermis G LU Glutami c acid E PI Epidermis J UV Juvenile M ANOVA Multivariate analysis of variance M SB Mean sum of squares between individuals M SW Mean sum of squares within individuals P HE Phenylalanine P LA Plasma R AAN Regin Autnoma del Atlntico Norte (Northern Atlantic Auton omous Region) R AAS Regin Autnoma del Atlntico Sur (Southern Atlantic Autonomous Region) R BC Red blood cells S CL Straight carapace length S D Standard deviation S RC AA Source amino acid T EF Trophic enrichment factor T NW Total niche width

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14 T P Trophic positio n T R AA Trophic amino acid W IC Within individual component of variation

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15 Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosoph y INTERPRETING SEA TURTLE TROPHIC ECOLOGY THROUGH STABLE ISOTOPE ANALYSIS By Hannah B. Vander Zanden August 2012 Chair: Karen Bjorndal Major: Zoology Endangered sea turtles spend most of their lives in marine habitats where they are difficult to acc ess and monitor. As we confront current and future anthropogenic threats to sea turtle populations, it is critical to understand the resources and foraging areas used by these animals in developing management plans. My work has focused on the ecology of green turtle and loggerhead foraging aided by the tool of stable isotope analysis. Naturally occurring stable isotopes of carbon and nitrogen in tissue samples act as indicators to trace the diet and habitat used by individuals prior to the sampling perio d. First, I examine d potential effects of individual variat ion on these isotope values as well as the offset in stable isotope ratios between four green turtle tissues and the diet. Individual variation was quite small, and discrimination factors differ ed with life stage, and these measurements can help to better interpret stable isotope data from wild populations. Next, I examine d the isotopic patterns in green turtles at nesting and foraging areas to assess feeding patterns and population connectivity i n Caribbean turtles. I found that the stable isotope values of individuals are highly influenced by the location in which they forage, and these differences can aid in determining the

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16 proportion of turtles in a nesting population that originated from a pa rticular foraging area. I also examine d the long term patterns in loggerhead and green turtle resource use through the chronological records that are contained in scute (the keratin layer of a adult loggerheads and green turtles demo nstrate d long term individual consistency in stable isotop e values that is indicative of high fidelity to foraging grounds. Th e temporal consistency and degree of specialization var ied with life stage in green turtles but can help to identify the ecologic al roles and habitat specificity for these species. Together, this research aids in understanding the trophic ecology of these species Information on where and what sea turtles eat is critical to protecting the areas they use most and for assessing the ri sk of encountering anthropogenic threats such incidental capture in fisheries or oil spills.

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17 CHAPTER 1 BACKGROUND OF SEA TURTLE BIOLOGY A ND STABLE ISOTOPE EC OLOGY Sea Turtle Life History Sea turtles have complex life histories, shifting between habitats and diets as well as moving among geographically separated foraging grounds and reproductive areas over their long life spans. The intricate life histories of sea turtles pose complications to study and conserve them. Six of the seven species of sea tur tles are listed as endangered or critically endangered mainly as a consequence of anthropogenic threats and slow population recoveries following declines. Sea turtles are tied to terrestrial habitats for reproduction, which facilitates research on nestin g females, but has left gaps in our knowledge of adult turtles in foraging habitats and in other life stages. Females spend only a fraction of their lives to deposit eggs on nesting beaches (Miller 1997) These eggs hatch approximate ly two months after their deposition, and hatchlings move from nesting beaches to oceanic habitats via major ocean currents, commencing (1987) Young oceanic loggerheads and green turtles, the focal species of this research, are likely opportunistic consumers, feeding on invertebrates in the sargassum mats that provide both shelter and food (Bjorndal 1997a, Bolten 2003, Reich et al. 2007) As young turtles become large enough to swim long distances, they undergo ontogenetic shifts and recruit to coastal, or neritic, areas, often initiating drastic d ietary changes. Juvenile and adult coastal foraging grounds may be separate, with areas that serve as transitional or developmental habitats for smaller turtles (Meylan et al. 2011) Additionally, some populations of green turtles and loggerheads maintain oceanic foraging strategies even through adulthood (Hatase et al. 2002, 2006) Adults exhibit high fidelity to foraging

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18 grounds (Lohmann et al. 1997) and undergo regular migrations between foraging and nestin g areas, often returning to the region of their birth (Miller 1997) Diet is the most basic interaction between an organism and its environment as resources are consumed and nutrients cycle through food webs. There is a need to underst and resources used by sea turtles and the roles they play in ecosystems, particularly for ecosystem based conservation approaches and for determining consequences in ecosystems when sea turtles are lost (Bjorndal & Jackson 2003) The focus of my research has been to enhance our understanding of nutritional ecology in green turtles and loggerhead s and how these roles might vary through life stages. Each portion of my dissertation has utilized stable isotope analysis as a tool to uncover characteristics of diet and habitat use in green turtles and loggerhead sea turtles. Stable Isotope Ecology Sta ble isotopes are increasingly being used as a tool in ecological studies, though much of the early uses were in the fields of geochemistry and paleooceanography to study element cycles, trace rock sources, and reveal past climatic conditions. Applications of natural abundances of stable isotopes in community ecology, landscape ecology, and ecosystem ecology include studies of food webs, animal migration, diet, and isotope circulation in the biosphere. Isotopes are forms of elements that differ in the numbe r of neutrons while maintaining the same number of protons and electrons. Radioactive isotopes decay over time as a result of severe imbalances of neutrons and protons, whereas stable isotopes are energetically stable and do not undergo a decay process as a result of smaller differences in the number of neutrons and protons. Stable isotopes comprise less than 10% of known isotopes (Fry 2006) and few are used in ecological

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19 applications: mainly hydrogen, carbon, nitrogen, oxygen, and sulfur. In my work, I have used carbon and nitrogen stable isotopes, with heavy is otopic forms that comprise only a small fraction of the natural abundance of the element (Table 1 1). Stable isotope ratios are measured through isotope ratio mass spectrometry (IRMS), with the earliest mass spectrometer dating back to more than a century ago (Michener & Lajtha 2007) Solid samples are typically combusted in an elemental analyzer to separate the elements and enter the mass spectrometer in a gaseous form. Analysis of the abundance of heavy and light forms of an element relies on the difference in mass between isotopes, as gas molecules are ionized and then accelerated with a magnet to generate an electric fiel d and bend the ion beams. The molecules separate because of inertia, bending more with smaller masses, and ion collectors, or Faraday cups, measure a voltage for both heavy and light beams. The signal strength is converted to a ratio and then compared to an international standard to calculate a (delta) value (Table 1 1). Because stable isotopes occur in such small frequencies, the delta notation provides a comparison to a standard and eliminates the resulting small numbers by multiplying values by 1000. is denoted with the symbol value indicates a sample that has more of the heavy isotope than the standard, and a negative value indicates a sample that has less of the heavy isotope than the standard. Stable isotopes can affect element cycling during kinetic reactions as a consequence of the differences between molecules with heavy or light isotopes. Heavy isotopes react more slowly because of the additional energy tha t is needed to activate

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20 the reaction or break bonds with other atoms. These properties of heavy isotopes result in fractionation, or a differential concentration in isotopes between the reactant and product. The frequent occurrence of isotopic fractionat ion in biogeochemical reactions results in biological, environmental, and geographical distributions of stable isotopes that give rise to their utility as natural tracers. For example, the fractionation process that causes heavy isotopes in liquid precipit ation to fall out first as moisture moves inland from oceans creates a chemical gradient, or isoscape, across continents. Thus, animals that move across the terrestrial isoscape can be tracked to a source through hydrogen isotopes (Hobson & Wassenaar 1996, Hobson et al. 1999) In the ocean, fractionation differences occur with varying temperature in carbon fixation by phytoplankton, creating isotop ic gradi ents in ocean basins that vary with latitude (Goericke & Fry 1994) Additionally, carbon isotopes in marine primary producers vary with distance from land as a result of the different fractionation processes in macroalgae, seagrass, and phytoplankton that dominate in coastal vs. oceanic regions (France 1995) While such processes result in large scale geographic gradients, fractionation that occurs at the level of an individual is also key to interpreting stable isotope ratios. The stable isotop e values of a consumer are directly influenced by the assimilated diet, and the fractionation process in metabolic reactions causes consumers to systematically differ from their diets, permitting the calculation of trophic level, for example (DeNiro & Epstein 1981, Minagawa & Wada 1984) Many of these processes enable stable isotope analysis to be an appropriate tool for understanding sea turtle ecology.

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21 A pplying Stable Isotope Analysis to Sea Turtle Ecology The fractionation process that results in consumer diet differences in isotope ratios is termed discrimination. Typically, carbon discrimination factors are small (DeNiro & Epstein 1978) whereas the nitrogen discrimination factors are larger and have typically been used to estimate trophic position, particularly when the baseline is known (DeNiro & Epstein 1981, Post 2002) In addition, inherent variation in tissues due to physiological differences among individuals can affect the isotope ratios in a tissue as well a s the discrimination factors, even under controlled conditions. Both inherent variation and discrimination factors are critical to evaluate stable isotope data in wild populations, to estimate trophic level, or to reconstruct diet through mixing models. In Chapter 2, I provide a measure of inherent variation i n four green turtle tissues to determine the variance that is as result of physiological differences among individuals. The Cayman Turtle Farm in Grand Cayman provided an opportunity to sample gree n turtles in two life stages (juveniles and adults) that were maintained on an isotopically consistent diet. The tissues sampled included: epidermis, dermis, red blood cells, and plasma. The measured inherent variation differed among tissues, but was sma ll compared to the variation in a wild population, indicating that most isotopic variation in wild green turtle populations can be attributed to diet and habitat differences rather than individual physiological differences. Second, I calculate the discrim ination factors for each of the four tissues to compare between life stages and among other sea turtle species Both l ife stage and species affect these discrimination factors, highlighting the need for using appropriate values to interpret trophic level and diet from stable isotope data in wild populations

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22 As stable isotope analysis is increasingly being used to assess the foraging patterns of endangered and cryptic species such as sea turtles, this research provides valuable information to interpret d iet and habitat use from stable isotope data more accurately. Chapter 2 has been accepted for publication. The citation will be: Vander Zanden HB, Bjorndal KA, Mustin W, Ponciano JM, Bolten AB. Inherent variation in stable isotope values and discriminati on factors in two life stages of green turtles. Physiological and Biochemical Zoology. Understanding potential variation contributing to green turtle tissue was important in the next portion of my research (Chapter 3), which focused on understanding diet and habitat use in a green turtle nesting population that exhibited a wide range of isotopic values. To determine how foraging grounds vary, I assessed the isotopic niche as a proxy for the ecological niche of green turtles at five foraging grounds across the Greater Caribbean in addition to the nesting beach in Tortuguero, Costa Rica. I also demonstrate the utility of the isotopic niche in estimating the proportion of the nesting population that forages at specific sites to assess population connectivity The wide isotopic niche observed in the nesting population suggested potential dietary differences among turtles, but without isotopic information a bout the base of the food web, this required further investigation. With bulk and compound specific sta ble isotope analysis of amino acids, I determined that primary producer variation, rather than trophic feeding differences contribute to the isotopic variation. This improves our understanding of green turtle foraging and ecological roles in the Caribbean and these approaches can be applicable for deciphering stable isotope data from other wide ranging marine species. Chapter 3 has also been submitted for publication. The

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23 citation will be: Vander Zanden HB, Arthur KE, Bolten AB, Popp BN, Lagueux CJ, Har rison E, Campbell CL, Bjorndal KA. Trophic ecology of a green turtle breeding population. In Chapters 4 and 5, I investigate long term diet and habitat use with consecutive layers of scute tissue in loggerheads and green turtles. Scute is the keratin mat erial covering sea turtle carapaces, and single samples can be sectioned to examine a record of resource use through time. This methodology can provide information about the consistency in diet and habitat use as well as the degree of individual specializ ation within the population. I focus on adult loggerheads in Chapter 4 and present the results in the context of a conceptual model comparing isotopic niches in specialist and generalist populations. The turtles sampled in the study nested in Florida bu t originated from highly dispersed foraging areas. While the population has a wide range in isotopic values, individuals are highly consistent through time, likely reflecting a high degree of habitat fidelity. This study has been published, and the citat ion is: Vander Zanden HB, Bjorndal KA, Reich KJ, Bolten AB (2010) Individual specialists in a generalist population: results from a long term stable isotope series. Biol ogy Lett ers 6:711 714 In Chapter 5, I examine the consistency in diet and habitat use of green turtles in three life stages: oceanic juveniles, neritic (or coastal) juveniles, and adults. Oceanic juveniles were expected to be more variable in their isotopic records as a result of the opportunistic foraging and nutritional stochasticity th at is thought to occur in this life stage, whereas neritic juveniles and adults in the Caribbean were expected to be more consistent in their isotopic records because of the reliable seagrass foraging habitat.

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24 Both temporal consistency and individual spec ialization varied among life stages. Oceanic and neritic juveniles trended toward less temporal consistency in resource use with less individual specialization than adults. As major consumers in the habitats they occupy, understanding their ecological ro les and foraging consistency is important to properly providing management strategies for each age class. Together, these studies contribute to understanding sea turtle trophic ecology. In the final chapter (Chapter 6), I review how this work has advanced the field of sea turtle biology, and how future research efforts might be directed to aid in understanding the ecology of these endangered species.

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25 Table 1 1. Abundance and standards used for stable is ot opes used in this research (adapted from Michener & Lajtha 2007) Element Isotope Abundance Relative mass difference (%) International standard Carbon 12 C 13 C 98.892 1.108 8.3 Vienna Pee Dee Belemnite (VPDB) Nitrogen 14 N 15 N 99.365 0.365 7.1 Atmospheric nitrogen (air)

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26 CHAPTER 2 INHERENT VARIATION IN STABLE ISOTOPE VALUES AND DISCRIMINATION FACTORS IN TWO LIFE STAGES OF GREEN TURTLES Introduction Stable isotope analysis is commonly used to investigate consumer foraging patterns in ecological studies. Dietary reconstructions through mixing models and trophic level estimations rely on diet tissue discrimination factors (t he difference between stable isotope values of More recent applications using carbon and nitrogen stable isotope composition s ( 13 C and 15 N) to examine trophic niche and specialization rely on measures of stable isotope va riance within the population (Layman et al. 2007b, Araujo et al. 2007, Newsome et al. 2007, Vander Zanden et al. 2010) The isotopic niche is used as a proxy for ecologica l dimensions of resource use because the stable isotope ratios in the tissue of an organism represent the assimilated diet (Layman et al. 2007a, Vaudo & Heithaus 2011) Additionally, specialization can be inferred by examining the isotopic variation of a population or a n individual through time L ow variation indicates specialization whereas substantial variation indicates generaliza tion (Martnez del Rio et al. 2009a, Bearhop et al. 2004, Newsome et al. 2009, Vander Zanden et al. 2010) In many studies, i sotopic variation is attributed to diet and habitat differences but can also r esult from variation in the isotopic composition within a prey species, inherent variation in the consumer, and measurement error (Bea rhop et al. 2002, Matthews & Mazumder 2004, Phillips & Eldridge 2006, Barnes et al. 2008) Inherent variation in stable isotope values (hereafter referred to as inherent variation) is a consequence of isotopic deviations that arise from individual differ ences in physiology despite consuming the same diet and experiencing controlled conditions Although not often

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27 quantified, inherent variation could substantially affect conclusions based on stable isotope data. Inherent variation can depend on the specie s, life history stage, and environment (Barnes et al. 2008) yet measurements of such variation from animals on controlled diets are sparse (Matthews & Mazumder 2004, Sweeting et al. 2005, Barnes et al. 2008, Seminoff et al. 2009) In one case, inherent variation comprised a large portion of the isotopic variance measured in a wild population of sea bass Dicentrachus labrax (Barnes et al. 2008) Therefore inherent variation should be considered when generating inferences about foraging patterns in wild populations. If it is assumed t hat all isotopic variation observed is a result of differences in diet and habitat use, then the resulting isotopic niche or level of generalization may be overestimated. Diet tissue discrimination is represente tissue die t and results from processes such as fractionation during metabolic transformations and isotopic routing (Martnez del Rio et al. 2009b) Accurate diet tissue discrimination factors are essential to estimating trophic level and diet reconstruction, and variation in the discrimination factor should be accounted for in mixing mode ls (Post 2002, Wolf et al. 2009) Many studies have used gen eralized discrimination factors due to the lack of species specific values, yet the use of such values can lead to large errors or meaningless results in the output of mixing models (Caut et al. 2009) Consumer tissues are often enriched in 15 N and 13 C compared to their diets (DeNiro & Epstein 1978, 1981, Post 2002) though d iscrimination factors may vary with life stage, environment, form of nitrogenous waste excretion, taxon, species, tissue, diet quality, and diet isotopic composition (Vander Zanden & Rasmussen 2001, Vanderklift & Ponsard 2003, Caut et al. 2009) The commonly used diet tissue discrimination value

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28 for nitrogen ( 15 N ) (DeNiro & Epstein 1981, Post 2002) Values of 13 C are 15 N values, resulting in a reduced trophic shift in 13 C values as nutrients are transferred through the food web (DeNiro & Epstein 1978) The first objecti ve of this study was to quantify the inherent variation in a captive population of green turtles ( Chelonia mydas ) that were fed a consistent diet I examine d the variation in stable isotope values a measure of inherent variation in four tissue types (epid ermis, dermis, serum, and red blood cells) and two life stages ( large juveniles and adults). I then compare d this measure of inherent variation in epidermis to the isotopic variance observed in a wild population. The second objective of this study was to measure discrimination factors for each of the four tissues in both juvenile and adult green turtles maintained on a n isotopically consistent diet. Furthermore, I incorporate d the measure of inherent variation in to estimates of the discrimination factors I also compare d the discrimination factors measured in this study with other sea turtle species. Materials and Methods Study C onditions G reen turtles were housed at the Cayman Turtle Farm Ltd. in Grand Cayman, British West Indies (CTF) These turtles are descendants of a mixed breeding stock comprised of turtles from at least four nesting populations (Wood & Wood 1980) Adults ranged from 10 to approximately 70 years of age, from 92 to 110 cm curved carapace length (CCL), and from 75 to 186 kg. The large juveniles were approximately 4 to 6 years of age and had been raised in captivity. Their size ranged from 64 to 92 cm CCL and 3 0 to 63 kg At CTF, large juveniles grow at substantially higher rates (about 14 cm CCL per year) than the same size class in the wild (Wood and Wood 1993; Bjorndal et

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29 al. 2000), and adults at CTF grow very little, if at all, after sexual maturity (Wood & Wood 1993) The turtles were fed an extruded floating pellet diet manufactured by Southfresh Feeds (Alabama, USA) at 0.5% body weight per day, for four years prior to sampling The feed consists of at least 36% crude protein, 3.5% crude fat, 12% moisture 6% crude fiber, and 1% phosphorus. A complete list of the pellet ingre dients is included in the Appendix The diet is highly digestible, and a similar diet (35% protein and 3.9% fat) had a dry matter digestibility of 85.9% and a protein digestibility of 89.4% (Wood & Wood 1981) T he turtles were assumed to be at isotopic equilibrium with the diet. Juveniles and adults were maintaine d in tanks or an artificial pond The water intake pipes for each are directed to create a slow, circular current against which the turtles swim. They are almost constantly in motion during daylight hours, with resting periods at night. The maximum dept h of the adult pond is 5.2 m with an artificial beach available for females to lay eggs. The depth of the juvenile tanks is 0.9 m. Sample C ollection D uring April and May 2010 t issue samples were collected from 30 adult female green turtles and from 40 la rge juvenile green turtles. Blood samples of 2 8 mL were drawn from the carotid arteries using sterile 16G x 2" IV catheters (SURFLO I.V. Catheters) and were immediately transferred to 9 m L Draw CORVAC serum separator tubes. Serum and red blood cells wer e separated by centrifugation at 2195 g and frozen separately at 20C until analysis. Skin samples were taken with 6 mm Miltex sterile biopsy punches in the region between the front flipper and the head just below the carapace and placed in 70% ethanol. Isotope values of sea turtle epidermis preserved in ethanol were not different from those that were dried at 60 C indicating

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30 that the preservation method does not affect the tissue stable isotope ratios (Barrow et al. 2008) At the time of sample collection, CCL was measured from the anterior midpoint of the nuchal scute to the posterior most tip of the rear marginal scutes and most individuals were weighed. Body cond ition index was calculated as [mass / CCL 3 )] 10 4 with mass in kg and CCL in cm (Bjorndal & Bolten 2010) At the time of tissue sampling, two diet samples of approximately 100 g from the same commercial batch were set aside for stable isotope analysis. The manufacturer produces feed approximately once per month. The diet was specifically formulated for the CTF and is held as constant as possible by the manufacturer. Although there might be slight isoto pic differences in different food lots, I a m confident that these are minimal. Because all turtles are fed daily from the same lots, any differences observed in the captive population would not be a result of the different lots, as they would have experie nced the variation equally. Sample P reparation and I sotope A nalysis Serum and red blood cell samples were thawed, dried at 60C for 24 hours and homogenized with a mortar and pestle to a fine powder. Skin samples were rinsed in distilled water ; epidermi s was removed from the dermis with a scalpel. A small portion of the dermis closest to the skin surface was sub sampled to provide the dermis sample. Both dermis and epidermis samples were homogenized by dicing with a scalpel and then dried at 60C for 2 4 hours. Diet samples were ground in a Wiley mill to <1 mm particle size Tissue samples weighing 0.5 0.6 mg and diet samples ranging from 0.45 1.5 mg were analyzed for carbon and nitrogen isotopes at the University of Florida Department of Geological Sciences Light Isotope Lab. Samples were combusted in a n

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31 ECS 4010 e lemental analyzer ( C ostech) interfaced via a ConFlo III device to a Delta Plus XL isotope ratio mass spectrometer (ThermoFisher Scientific) The standards used for 13 C and 15 N were Vienn a Pee Dee Belemnite (VPDB) and atmospheric N 2 respectively. Delta notation is used to express stable isotope abundances, defined as (2 1) where R sample is the ratio of heavy to light isotopes ( 13 C/ 12 C or 15 N/ 14 N) in the sample and R standard is the isotope ratio of the corresponding international standard. The reference material USGS40 (L glutamic acid) was used as a calibration standard in all runs with a stan 13 15 N ( n = 32). Repeated measurements of a laboratory reference material, loggerhead sea turtle ( Caretta caretta ) scute was used to examine consistency in a homogeneous sample with similar isotopic compositio n to the tissue samples in this study. The standard deviation of the loggerhead scute was 13 15 N ( n = 13). A subset of six dermis samples weighing approximately 1.0 mg plus diet samples weighing 3.0 4.5 mg was also analyzed for dry mass percent carbon (C) and nitrogen (N) to calculate the C:N ratio Lipids were extracted from a different subset of six dermis samples using petroleum ether in an accelerated solvent extractor (Dionex ASE300) and analyzed for carbon and nitrogen isotopes to examine the effect of lipids on the isotope composition and isotopic variation Data A nalysis Ten m ultivariate normal models were fit to the carbon and nitrogen isotope data to examine how to best group the data while considering means and va riances among

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32 groups (Table 2 1). Four hypotheses were examined to determine if the data were best described by considering: 1) all samples together, 2) grouped by life stage (large juveniles, adults), 3) grouped by tissue type (epidermis, dermis, serum, red blood cells) or 4) grouped by both life stage and tissue type. Three model parameterizations were applied to each hypothesis (except the first) to create a total of ten models ( Table 2 1 ). The second and third parameterizations were not applied to t he first hypothesis because there was only one group, meaning that creating a pooled variance or centered mean had no effect. In these parameterizations, the vector of mean s the variance s or both were allowed to differ in the resulting multivariate norma l likelihood function (Johnson & Wichern 2002) of the observed data. The ML estimates also correspond to the vector of arithmetic means and the sample variance covariance matrix (Johnson & Wichern 2 002) The four tissues were assumed to be independent samples, and the analysis diagnostics (residuals) were examined to ensure there were no major departures from the model assumptions. The first hypothesis was a null model that assumed the variability among all life stages and tissue types was best described with only one set of parameters: a single set of means and a single variance covariance matrix. In the second hypothesis, the data were divided by life stage to determine if adult and large juveni le samples were different in their isotopic values, irrespective of tissue type. Thus, the samples were assumed to come from just two sampling multivariate normal models with different mean vectors and variance covariance matrices. In the third hypothesi s, each tissue type was considered separately, though adult and large juvenile values of the same

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33 tissue type were grouped. Hence, four different multivariate normal sampling models were needed to explain the data. In the fourth hypothesis, the samples w ere divided by both life stage and tissue type, creating eight groups. Thus, the joint likelihood of all the data needed for parameter estimation becomes the product of eight different multivariate normal probability density functions. Model selection w as carried out using Bayesian information criterion (BIC) (Raftery 1995) Adding more parameters to a fixed model may improve the fit of the model, but the tradeoff is that it increases uncertainty in the estimation process. The BIC includes a term to penalize the maximized likelihood score with a quantity proportional to the n umber of parameters used by the model. The BIC was calculated as: where is likelihood function evaluated at the ML estimates, p is the number of parameters, and q is the sample size. To evaluate whether there were differences in means and variances among the eight groups, p airwise comparisons were made between the mean vectors and variance covariance matrices for a subset of all possible pairs Each of the four tissue types were compared within the same life s tage, but comparisons between life stages were made only for the same tissue type. In these pairwise comparisons, M L estimates and BIC values were calculated first assuming that the observed samples all came from a single sampling multivariate normal mode l and thus could be combined for the parameter estimation process and then assuming that the samples from the two different groups actually resulted from two separate multivariate normal sampling models where either the means, variances or both were assum ed to differ D ifference s

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34 were calculated as BIC combined BIC separate A value greater than 2, 6, or 10 correspond s to positive, strong, or very strong evidence respectively, for favoring the separate model over the combined model (Raftery 1995) Therefore, t wo groups were considered significantly different in their bivariat e means or variances if > 2. Negative values occurred when the BIC separate was larger than the BIC combined tissue diet for carbon and nitrogen Variance from both the diet samples and the tissue was integrated into estimates of the discrimination factors through parametric bootstrapping. Normal distributions were used to represent the 13 C and 15 N values for the diet and each tissue for both life stages The mean diet tissue discrimination values 1 SD were calculated by running 50,000 iterations The relationships between body condition index and 13 C dermis values were using R (R Development Core Team 2011) Result s The variance in the stable isotope values, the inherent variation of each tissue type and life stage differed among some tissues and life stages ( Tables 2 2, 2 3 2 4 ). The highest variance in the adult tissues occurred in dermis which was significant ly greater than the variance in other tissues, and the lowest variance in adult tissues was observed in red blood cells. For juvenile tissues the highest variance was also observed in dermis with the lowest variance in serum. The high variance in adult dermis was influenced by several points that exhibited high 13 C values. Re analysis of stable isotope ratios in those samples indicated the

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35 values are accurate To evaluate the influence of lipid content on variation or discrimination factors in dermis, the C:N ratio was measured and determined to be 2.8 in a subset of six samples Additionally three of the extreme dermis points, and three randomly selected dermis samples were lipid extracted and compared to the non lipid extracted tissue using paired t tests. There was no significant difference in either the 13 C or 15 N values ( t C = 0.36, df = 5, p C = 0.73; t N = 0.86, df = 5, p N = 0.43). F or all adult turtles, 13 C values in dermis were significantly correlated to body condition d as measures of health. While the condition index ranged from 0.5 to 1.9 in all turtles, the six adults with the highest dermis 13 C values (> measures > 1. 4. Pairwise comparisons between tissue means reveal ed significant difference s among all tissues ( Table 2 3 ), which led to differences in discrimination factors among tissues. Discrimination factors between diet and turtle tissues ( ) in adults ranged fr om 48 to 4.9 3 Table 2 5 ). Discrimination factors in large 6 to 4. 15 nitrogen which were substantially larger than discrimination factors previously r eported for juvenile green turtles ( Table 2 4 13 C 15 N values were smaller in all tissues. Diet samples had a C:N ratio of 7.5 (mean C = 42.7%, mean N = 5.7%, n = 4). Mean 13 C and 15 N values of the di et were 2 3.05 and 2. 53 n = 12, Table 2 2 ).

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36 To determine whether the data were best divided using both life stage and tissue type, four hypotheses with different data groupings were examined. BIC values decreased with the addition of more groups in each successive hypothesis, indicating an improvement in describing the data even though more parameters were estimated ( Table 2 1 ). Among the four hypotheses, t he lowest BIC values were obtained when the data were grouped by tissue type and lif e stage, indicating that eight groups we re most appropriate to divide the data ( H ypothesis 4, Table 2 1 ). Each of these hypotheses was examined with three different model parameterizations to examine the relative importance of the mean or the variance in reducing (thus improving) the BIC score The first model parameterization included estimates of both the mean vector and variance covariance matrix. The four different groupings using the first parameterization are plotted with ML estimates and confidenc e ellipses in Figures 2 1 and 2 2 In the second parameterization, the mean was estimated in each group using a pooled variance, and in the third parameterization, the variance was estimated for each group using a centered mean. The first hypothesis cou ld not be compared across parameterizations. For the other three hypotheses, the second model parameterization with pooled variance yielded higher BIC values than the first parameterization, indicating a pooled variance among the groups does not perform a s well in the model ( Table 2 1) The BIC values of the third parameterization compared to the second parameterization were higher for hypothesis 2 and lower for hypotheses 3 and 4. The lowest BIC value overall occurred in h ypothesis 4 with t he third para meterization, indicating that the variance is more influential in driving the group differences.

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37 Discussion Inherent V ariation M easures of inherent variation can be informative for field studies. Because the inherent variation differed among tissues, t he se measures can be used to select a tissue to minimize inherent variation and better understand isotopic variation in wild populations. For example, a population of resident juvenile green turtles in a small foraging area in The Bahamas had a range of 5.4 13 15 N values from epidermis samples (Bjorndal and Bolten 2010) In the captive population from this study, the epidermis ranges we 13 15 N. Moreover, the variance in both 13 C and 15 N values is much smaller in the captive population, as indicated by the size of the bivariate confidence ellipses ( Figure 2 3). In this example, inherent variation does not form a large part of the isotopic variance in the wild population, as has been observed in other studies (Barnes et al. 2008) Therefore, it is unlikely that physiological differences in the wild population would create the observed variation in isotopic values, but rather, individuals are probably using different diets or habitats or the prey species exhibit intraspecific variation As additional studies begin to examine specialization in foraging through stable isotope consistency and isotopic niche space of disti nct populations, these measures of inherent variation can be used to inform the baseline variation that is a result of individual differences, and thus, additional variation can be attributed to differences in diet and resource use with greater confidence. I am uncertain about the cause of the wide range in dermis 13 C values for adults in this study but lipids do not appear to be responsible for the observed range The

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38 measured C :N ratio of dermis in this study (2.8) falls below the cutoff of 3.5 for a quatic animals, indicating that lipid content i s likely below 5% and would not influence the 13 C values (Post et al. 2007) Also removal of lipids did not result in significant differences for 13 C values A relationship was observe d between the dermis 13 C values and the condition index. If the condition index i s an indicator of health or if it changes with reproductive status, these factors may influence the range in adult dermis 13 C values. Based on these results, I would discourage the use of dermis as a sampling tissue. In this study, large j uveniles gene rally had lower variance compared to the corresponding tissue in adults Growth may affect the inherent variation, as it can also affect the discrimination factors (see life stage section of this discussion). T he isotopic variation previously reported in juvenile green turtle tissue s (Seminoff et al. 2006) is similar to my study ( Table 2 2 ). Addit ionally, inherent variation was quantified in a captive population of juvenile leatherbacks Dermochelys coriacea (Seminoff et al. 2009) and the measures of variance in the juvenile green turtles from this study we re lower in all tissues ( Ta ble 2 2 ). Discrimination F actors M ean discrimination factors of 0 1 for 13 15 N were reported by early studies (DeNiro & Epstein 1978, Minagawa & Wada 1984) and confirmed by recent reviews (Vand er Zanden & Rasmussen 2001, Post 2002) Nevertheless, discrimination factors have been observed to change with an array of variables. Because species or tissue specific discrimination factors are lacking these standard discrimination values continue t o be applied to isotopic mixing models for dietary reconstructions or trophic level estimations (Caut et al. 2009) Small changes in

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39 discrimination factors can lead to substantial differences in the output of these mixing m odels (Ben David & Schell 2001) ; therefore, it is critical to provide species and tissue specific measures. Stable isotope analysis has been increasingly used to investigate sea turtle foraging patterns, because of the advantages of this technique which enable sampl ing these long lived, migratory animals with cryptic life stages (Reich et al. 2007, Arthur et al. 2008, Vander Zanden et al. 2010) Additionally mixing models have been applied to reconstruct sea turtle diets (Wallace et al. 2009, McClellan et al. 2010, Lemons et al. 2011) Tissue The means and variances of 13 C and 15 N were distinct among tissue type s and life stage s These differences in tissue means transl ate into discrimination factor differences. The inherent variation observed in each tissue was also incorporated in to the standard deviations of the discrimination factors. For example, d ermis had the largest inherent variation among the four tissue type s as well as the largest standard deviations in the estimates of discrimination values. Consistent differences in 15 N values have been observed for the same tissues across a variety of species, likely because of different metabolic properties that are use d to create and maintain these tissues (Caut et al. 2009) Such differences might be caused by the amino acid content of each tissue (Martnez del Rio et al. 2009b) While some amino acids remain close to the isotopic composition of the diet, others are enriched through metabolic processes (McClelland & Montoya 2002, Popp et al. 2007) resulting in varying 15 N values among amino acids 15 N values of plasma > hair > red blood cells (Caut et al. 2009) similar to the pattern 15 N values of serum > epidermis (a keratin

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40 based structure) > red bl ood cells. The mean difference between plasma/serum and 15 (Caut et al. 2009) and was large juveniles. Differences a mong tissues in 13 C values can be influenced by amino acid composition as well as lipid content, as lipids tend to be depleted in 13 C (DeNiro & Epstein 1977) Discrimination factors for carbon have been shown to vary with methods of sample preparation such as lipid removal or acidification (McCutchan et al. 2003) In this study, I did not remove lipids from either the tissue or diet samples but in the subset of dermis samples for which lipids were removed, there was no effect on the stable isotope values of the t issue Isotopic routing is a nother factor that may affect both nitrogen and carbon discrimination (Gannes et al. 1998) I am unable however, to evaluate the effects of isotopic routing in this study. Life stage. Nitrogen discrimination factors were larger in adults compared with the respective large juvenile tissues in this study The differences between life stages may be a result of protein balance di fferences rather than age, as a nimals with a 15 N values than animals that have a neutral or negative protein balance (Martnez del Rio & Wolf 2005) Protein balance is indicative of the efficiency of nitrogen deposition measured as the ratio between protein assimi lation and protein loss, and growing animals are expected to be in positive protein balance (Martn ez del Rio et al. 2009b) Large juveniles grow rapidly and adult growth is minimal (Wood & Wood 1993) The pattern in nitrogen discrimination factors in this study support s the predictions by Martnez del Rio and Wolf (2005) Studies comparing

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41 life stages or relative growth rates in other species have also reported patterns corroborating this prediction in red foxes V ulpes vulpes (Roth & Hobson 2000) Atlantic s almon Salmo salar (Trueman et al. 2005) and blue crabs Callinectes sapidus (Fantle et al. 1999) Unlike nitrogen discrimination factors, there is no empirical prediction for the relationship between growth rate and carbon discrimination factors. The differences between adults and large juveniles were relatively small for epidermis, dermis, and red 13 C values for the same tissue occurred in probably as a result of higher lipids in adults Females mobilize lipids for egg production, primarily vitellogenin (containing lipid triglycerides), which is synthesized in the liver and transported to the ovary in plasma (Hamann et al. 2003) Plasma triglyceride levels may increase up to six months prior to the breeding season and remain high throughout the nesting season (Hamann et al. 2002) The adults in this study were all sexually mature females and sampled just prior to the nesting season. Intraspecific and interspecific comparisons. A negative trend between diet isotope values and discriminati on factors has been observed across a wide range of taxa, though the trend was not examined in reptiles as a result of limited data (Caut et al. 2009) If this trend were sustained for reptiles, I would expect higher discrimi nation factors for juveniles in this study compared to those previously reported for juvenile green turtles (Seminoff et al. 2006) because of the lower 13 C and 15 N values in the diet of this study (Table 2). T he method proposed by Caut et al. (2008, 2009) to apply a diet dependent discrimination factor may be appropriate for reconstructing sea turtle

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42 diet s through isotope mixing models. At thi s time, however, insufficient reptile data are available to calculate diet dependent discrimination factors. Nutritional content of the diet particularly for nitrogen, may also affect discrimination factor s A positive trend between diet C:N 15 N values has been observed in a variety of species (Robbins et al. 2005) The feed used in this study had a higher C:N ratio than that used by Seminoff et al. (2006) (7.5 vs. 6.6). C onsistent with the pattern observed by Robbins et al. (2005) the higher diet C:N ratio 15 N value. Yet further investigation of this pattern through varied diets in a single mammalian species yielded no relationship between C:N ratios and 15 N values (Robbins et al. 2010) Rather, complementari ty of amino acids and diets composed of a mixture of items may contribute to variation in 15 N values (Robbins et al. 2010) In comparison with other sea turtle species, t 15 N values measured in large juveniles from this study are higher than what has previously been reported (Table 2 4). Besides possible dietary differences, growth rate differences are likely a major contributor to these discrimination value difference s. T he juveniles in this study were 15 N values (Martnez del Rio & Wolf 2005) The carbon discrimination factors were more variable among the sea turtle 13 C value observed for epidermis was in leatherbacks (Seminoff et al. 2009) for serum/plasma was in green turtles from this study, and for red blood cells was in loggerheads (Reich et al. 2008) (Table 4). This may be a result of differences in lipid concentration for each of the species, yet I am unable to make

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43 comparisons between potential lipid content, as C:N ratios were not measured i n all studies. Outcomes In summary, I found that inherent variation is both tissue and life stage dependent, and these results can be useful for more accurately estimating the degree of specialization and isotopic niche width in wild populations. Inheren t variation was only a small portion of the variance observed in the stable isotope composition of a wild population. In addition, diet tissue discrimination factors in sea turtles may vary with species, tissue type, diet, and growth rate, thus underscori ng the need for appropriate discrimination values in mixing models and trophic level estimations. I provide the first measure of discrimination factors for adult sea turtles. In juveniles, I believe the differences in discrimination factors compared to p revious studies in sea turtles may be attributable to differences in diet and growth rate Understanding the processes that influenc e isotopic discrimination and variance is fundamental to studies using stable isotope analysis to investigate foraging, beh avior, and ecological roles of wild populations

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44 Table 2 1 BIC values for the ten models Model parameterization Hypotheses 1. Mean and variance 2. Mean (with pooled variance) 3. Variance (with centered mean) 1. Null (all data in one group) 1331 .7 --2. Life stage (two groups) 1250.0 1306.5 1272.5 3. Tissue (four groups) 589.1 725.1 555.3 4. Life stage and tissue (eight groups) 365.3 589.2 286.5 The data were grouped according to four hypotheses in which all data were considered together or were grouped by life stage, tissue, or both. Three model parameterizations were considered in which the mean, variance, or both were allowed to differ in maximizing the function. The first model could not be considered with alternative parameterizatio ns

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45 Table 2 2. Mean 13 C and 15 this study and other juvenile sea turtle tissues at isotopic equilibrium reported from the literature. C. mydas ( this study) C. mydas a Der mochelys coriacea b 13 C (var) 15 N (var) 13 C (var) 15 N (var) 13 C (var) 15 N (var) Diet Diet (lipid extracted) 2 3.05 (0. 29 ) 2.49 (0.0 5 ) 19.03 ( 0. 97 ) 18.64 (0. 20 ) 6.24 (0. 24 ) 6.21 (0. 34 ) 17.71 (0.12) 17.76 (0.08) 8.64 (0.22) 8.59 (0.53) Adults n = 30 Epidermis 21.44 (0.08) 6.57 (0.14) Dermis 20.47 (1.14) 7.47 (0.29) Serum 22.80 (0.08) 6.70 (0.12) Red blood cells 22.75 (0.04) 5.01 (0.07) Juveniles n = 40 n = 8 n = 7 Epidermis 21.18 (0.03) 6.31 (0.11) 18. 54 ( 0.0 4 )* 9.00 (0. 32 )* 15.46 (0.24)* 10.50 (0.03)* Dermis 20.88 (0.05) 6.69 (0.16) Serum/Plasma 21.89 (0.02) 6.59 (0.08) 19.18 ( 0.05 ) 9.14 ( 0.03 ) 18.35 (0.21) 11.45 (0.14) Red blood cells 22.54 (0.03) 4.89 (0.09) 20.15 ( 0.03 ) 6.52 ( 0.04 ) 17.31 (0.05) 10.08 (0.03) Diet sample mean 13 C and 15 N values and variance are also included. The values in this study were reported for serum, but plasma was used in the other studies. Due to the similarity in these two tissues, they are reported o n the same line. (Var = inherent variation) a Seminoff et al. 2006 b Seminoff et al. 2009 *lipid extracted tissue

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46 Table 2 3. Pairwise comparisons among bivariate means. Adult EPI Adult DERM Adult SER Adult RBC Juv EPI Juv DERM Juv SER Juv RBC Adult E PI 62.8** 100.5** 132.3** 19.0* Adult DERM 93.7** 169.7** 97.2** Adult SER 111.3** 126.2** Adult RBC 9.1** Juv EPI 19.9** 138.6** 208.8* Juv DERM 178.4** 218.1** Juv SER 178.3** Juv RBC (Raftery, 1995) C omparisons that have no biological meaning a re not included. (EPI = epidermis, DERM = dermis, SER = serum, RBC = red blood cells, J uv =juvenile.) positive evidence for a difference between g ** strong

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47 Table 2 4. Pairwise comparisons among bivariate variance covariance matrices Adult EPI Adult DERM Adult SER Adult RBC Juv EPI Juv DERM Juv SER Juv RBC Adult EPI 30.7** 9.9 6.4 2.1 Adult DERM 33.1** 50.4** 68.5 ** Adult SER 6.4 7.0** Adult RBC 6.4** Juv EPI 8.3 8.9 12.0 Juv DERM 3.4* 6.1 Juv SER 9.9 Juv RBC ificantly different (Raftery, 1995) C omparisons that hav e no biological meaning a re not included. (EPI = epidermis, DERM = dermis, SER = serum, RBC = red blood cells, J uv =juvenile.) positive evidence for a difference between g

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48 Table 2 5. 13 15 N) measured in this study and for other sea turtle species reported from the literature C. mydas adults ( this study) C. mydas juveniles ( this study) C. mydas juveniles a Dermochelys coriacea juveniles b Caretta caretta hatchlings c Caretta caretta juveniles c Sample size 30 40 8 7 12 12 13 C Epidermis 1.62 0.61 1.87 0.56 0.17* 0.0 8 2.26* 0.61 Dermis 2.58 1.19 2.18 0.59 Serum/plasma 0.24 0.61 1.16 0.56 0.12 0. 08 0.58 0.53 0.29 0.20 0.38 0.21 Red blood cells 0.30 0.58 0.51 0.56 1.11 0. 17 0.46 0.35 0.64 0.7 3 1.53 0.17 15 N Epidermis 4.0 4 0.4 4 3. 77 0.4 0 2.80* 0. 3 1 1.85* 0.50 Dermis 4.9 3 0. 59 4. 15 0. 47 Serum/plasma 4.17 0.4 1 4.06 0. 37 2.92 0.0 8 2.86 0.82 0.32 0.09 1.50 0.17 Red blood cells 2.48 0 .35 2.3 6 0.37 0.22 0.0 8 1.49 0.76 0.25 0.30 0.16 0.08 All values are reported as means this study were reported for serum, though in all other studies the tissue used was plasma. Due to the similarity in the two tissues, they are reported on the same line. Discrimination factors were not measured for dermis in the other studies. Tissues were not lipid extracted unless noted. a Seminoff et al. 2006 b Seminoff et al. 2009 c Reich et al. 2008 *lipid extract ed tissue and diet lipid extracted tissue

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49 Fig ure 2 1. Results of three models using the first parameterization in which mean and variance are estimated. Solid symbols represent mean 13 C and 15 N values, whereas open symbols represent individual mea surements (see figure legends). Bivariate 95% confidence ellipses are drawn for each group; a dotted ellipse is used for the juvenile group. A ) The first model contains all data in one group and then data are grouped by B ) life stage or C ) tissue type. (RBC = red blood cells.) A

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50 Figure 2 1 C ontinued B C

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51 Figure 2 2. Results from the fourth hypothesis using the first parameterization, depicted separately to highlight the eight groups Solid symbols represent mean 13 C and 15 N values, wh ereas open s ymbols represent individual measurements (see figure legend). Bivariate 95% confidence ellipses are drawn for adults (solid lines) and juveniles ( dashed l ines ) Data are grouped by life stage and tissue type. (EPI = epidermis, DERM = dermis, SER = serum RBC = red blood cells, Juv = juvenile.)

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52 Fig ure 2 3. Comparison of isotopic variation in epidermis samples f rom juvenile green turtles. The inherent variation for the captive population ( n = 40) is considerably smaller than the variation observed in the wild population resident on a small foraging area off Inagua, Bahamas, for at least one year ( n = 42). Bivariate 95% confidence ellipses are included for each group. Wild population data are modified from Bjorndal and Bolten (2010)

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53 CHAPTER 3 TROPHIC ECOLOGY OF A GREEN TURTLE BREEDIN G POPULATION Introduction Sea turtles are highly migratory species with nesting populations composed of individuals from multiple foraging grounds, often separated by hundreds or thousands of kilometers (Harrison & Bjorndal 2006) Like many marine migratory species, sea turtles make regular migrations between foraging grounds and breeding areas a nd often show great fidelity to both areas (Lohmann et al. 1997) Thus, sampling a single breeding population can provide the opportunity to study the foraging ecology of females from widely dispersed foraging aggregations. For most marine turtle nesting populations, the distribution of f oraging grounds, the proportion of nesters from each foraging ground, and the variation in diets among and between foraging grounds are poorly understood but are important for the conservation of the breeding stock. Green turtles are the only herbivorous sea turtle species, though omnivory and carnivory are common among young juveniles using oceanic habitats (Bjorndal 1997a) In the Greater Caribbean, green turtles typically re cruit to neritic, or coastal, habitats by six years of age where they transition to a herbivorous diet as growing juveniles and continuing into adulthood (Bjorndal 1997a, Zug & Glor 1998, Reich et al. 2007) However, adult green turtles consuming primarily animal matter have been observed in other regions, mainly in the Pacific (Hatase et al. 2006, Amorocho & Reina 2007, Arthur et al. 2007, Lemons et a l. 2011, Rodriguez Baron et al. 2011, Burkholder et al. 2011) and some adult green turtles continue to maintain an oceanic, carnivorous foraging strategy as adults (Hatase et al. 2006) suggesting considerable flexibility in the diet of this species.

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54 Previous analyses of stomach and feces content indicate that Greater Caribbean green turtles are herbivorous w ith a diet composed predominately of seagrasses and/or algae (reviewed in Bjorndal 1997a) Small amounts of animal matter, primaril y sponges have been observed in Caribbean green turtles (Mortimer 1981, Bjorndal 1990) which could potentially contribute disproportionately to their nutrition, given the accessibility of nitrogen in sponges compared with seagrass (Bjorndal 1985) Green turtles in the Caribbean have not been observed to maintain an oceanic or carnivorous diet after the oceanic stage, yet evidence of this foraging strategy would suggest a new ecological role for the population. However, turtles using alternative foraging strategies might be missed as a re sult of the methods that have been used to evaluate their foraging ecology. Previous diet studies have occurred in known foraging habitats in near shore environments and would fail to identify turtles feeding offshore. Additionally, fishery dependent tag return data in the Caribbean originate primarily from coastal waters, therefore biasing recapture information to turtles that use neritic habitats (Trong et al. 2005) Stable isotope analysis has become increasingly advantageous for revealing resource use patterns in highly migratory marine vertebrates (Ruben stein & Hobson 2004) More specifically, the isotopic niche provides a metric with which to compare assimilated diet and habitat differences among and/or within populations (Layman et al. 2007a, Martnez del Rio et al. 2009a, Navarro et al. 2011) As a proxy for the ecological niche, the is otopic niche encompasses the range of two or more stable isotopic compositions in an aggregation or population and is influenced by what individuals consume (bionomic) as well as where they live (scenopoetic) (Newsome et al. 2007)

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55 Carbon isotope values ( 13 C) have been used as scenopoetic indicators mainly in terrestrial environments because they reflect that of the primary producers of a habitat, whereas nitrogen isotope values ( 15 N) have been used as bionomic indicators that (DeNiro & Epstein 1978, 1981, Post 2002) However, these distinctions are not always clear, as many other factors can influence 13 C and 15 N values at the base of the food web, particularly in marine environments (e.g., Hannides et al. 2009, Graham et al. 2010, Dale et al. 2011, McMahon et al. 2011, Values of 13 C and 15 N vary naturally with location as a resul t of biogeochemical processes that affect nutrient isotopic compositions and can create gradients such as those related to latitude or proximity to shore (Rubenstein & Hobson 2004, Graham et al. 2010, Somes et al. 201 0) Because the tissues of organisms reflect the isotope compositions of carbon and nitrogen in their habitats, stable isotope analysis can be used to infer geographical origins and differentiate among populations (Rubenstein & Hobson 2004, Ramos et al. 2009) Therefore, individuals sampled at breeding grounds can provide the opportunity to identify distinct isotopic feat ures of foraging areas and patterns of migratory connectivity (Cherel et al. 2006, 2007) Compound specific stable nitrogen isotope analysis of amino acids (AA CSIA) can determine whether va riations in bulk (total tissue) 15 N values are a result of differences in baseline 15 N values or differences in trophic position (e.g., herbivory vs. carnivory). Previous studies using AA CSIA have quantified trophic levels of marine organisms without h aving to characterize baseline 15 N values (Popp et al. 2007, Hannides et al. 2009, Dale et al. 2011, Semin off et al. 2012) which is particularly useful for systems in

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56 which 15 N values of primary producers vary spatially or temporally. This method relies on the 15 glutamic acid, le ucine, sensu Popp et al. 2007) are enriched in 15 N relative to prey presumably due to transamination and deamination reactions that cleave the carbon nitrogen bond (Chikaraishi et al. 2007) phenylalanine, sensu Popp et al. 2007) remain relatively unchanged in their nitrogen isotope composition due to an absence or reduced metabolic proc ess es that affect C N bonds (Chikaraishi et al. 2007) I assess the trophic ecology and foraging ground distribution of g reen turtles nesting at Tortuguero in northeast Costa Rica using bulk and compound specific isotopic analyses. I also investigate the potential for carnivory, and the ecological niche occupied by this Caribbean green turtle population. Tortuguero hosts t he largest rookery of green turtles in the Atlantic by an order of magnitude (Chaloupka et al. 2008) Recoveries of over 4 600 flipper tag s applie d to individuals nesting at Tortuguero indicate that the se turtles travel throughout the Greater Caribbean from the Florida Keys to Northern Brazil (Trong et al. 2005) The large majority of flipper tag returns come from seagrass beds of Nicaragua (Tron g et al. 2005) but this observation is likely biased by the take of turtles in this area. I assess both bionomic and scenopoetic contributions to carbon and nitrogen isotope compositions of the nesting population by comparing the isotopic niche of the n esting population and those of multiple foraging aggregations. I explore scenopoetic contributions to green turtle stable isotope values using isotopic variability in the primary producer, Thalassia testudinum which has been identified as the main compon ent of

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57 Caribbean green turtle diets (Bjorndal 1997a) I further define baseline and trophic contributions to bulk epidermis nitrogen isotopic composition of the nesting populati on using AA CSIA. Finally, I estimate the portion of the nesting population that may come from specific foraging grounds using the observed isotopic niche of foraging aggregations. Materials and Methods Sample C ollection and P reparation Epidermis samples were collected from 376 green turtles from one nesting beach (Tortuguero, Costa Rica) and five foraging grounds selected for geographic diversity within the Greater Caribbean: Union Creek, Great Inagua, Bahamas; Clarence Town Harbour, Long Island, Bahamas ; Puerto Cabezas, North Atlantic Autonomous Region Autonomous Region (RAAS) Nicaragua; and St Joseph Bay, Florida, USA (Figure 3 1). Only adult females were sampled at the nestin g beach. Adults and juveniles of both sexes were sampled from the two Nicaraguan sites; only juveniles were sampled in the other foraging grounds, although adults are known to forage in those areas (Table 3 1). Size is reported as curved carapace length (CCL) (Bolten 1999) In Nicaragua and Inagua, Bahamas alternate length measurements were taken from some turtles, and c onversions to CCL w ere made using linear regression equations from other turtles measured at those sites. For the Nicaragua sites, direct CCL measurements were available for 32 of the individuals s ampled. For the remaining 151 turtles, CCL values were derived from curved p lastron length (CPL) measurements based on a regression of 814 adult turtles encompassing the size range of the sample population (CCL = 1.089*CPL + 11.008 r 2 = 0.84 ) (Lagueux and Campbell, unpubl. data). For Inagua,

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58 direct CCL measurements were availabl e for 42 of the individu als sampled. For the remaining 20 turtles, CCL values were derived from straight carapace length (SCL) measurements based on a regression of 1421 juvenile green turtles encompassing the size range of the sample population (CCL = 1. 043*SCL 0.345 r 2 = 0.99 ) (Bjorn dal and Bolten, unpubl. data). Epidermis samples from turtles nesting at Tortuguero were collected from May to July (approximately the first third of the nesting season) in 2007 and 2009. Using estimates of epidermis turn over time (Reich et al. 2008) it was assume d that the isotopic composition of these samples reflects the diet in the foraging habitat during the months preceding migration to the nesting beach. Two females (Aurora and Chica) were also fitted with satellite transmitters, and the routes are available on the Sea Turtle Conservancy website (2012) Samples at foraging grounds were obtained from green turtles caught via net, hand capture, e n route to slaughter, or from stranded cold stun ned animals. Turtles were assumed to be residents at the foraging area in which they were captured using site specific criteria All turtles in the two Bahamian sites had previously been captured in the same location a year or more prior to the sampling date and were identified by flipper tags. In the two Nicaraguan foraging grounds, turtles were not sampled during the months in which migration to or from the nesting grounds occur s (i.e., sampling occurred January May). Outside of the migration period, green turtles found in these regions are likely residents (Campbell 2003) Samples from turtles in Florida were collected during a cold stunning event in January 2010, and at that site, juvenile turtles that could have recently recruited from ocean ic foraging grounds were

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59 excluded by selecting individuals that were > 31 cm CCL (the minimum size of recaptured turtles in the Bahamas sites, Table 3 1). Skin samples were collected from the neck region between the front flipper and head just below the ca rapace using a sterile 6 mm Miltex biopsy punch and were preserved in ethanol until processing. All skin samples were rinsed in deionized water and cleaned with an isopropyl alcohol swab prior to preparation. Epidermis was separated from the dermis using a scalpel blade, and the epidermis was diced and dried at 60C for 24 hours. Lipids were removed from epidermis using an ASE300 accelerated solvent extractor (Dionex) and petroleum ether solvent for three consecutive cycles consisting of 5 min of heating to 100C and pressurization to 1500 PSI, five minutes static, purging and then flushing with additional solvent. Healthy leaf blades of the seagrass T. testudinum were collected from three of the five foraging locations (Fig ure 3 1, Table 3 2). Epiphyt es were removed from blades with gloved fingers, and seagrass samples were dried at the field site or frozen for transport back to the laboratory. All s eagrass blades were dried in the laboratory at 60C for 24 hours and ground to <1 mm in a Wiley Mill. Sample A nalyses Isotopic compositions of bulk epidermis (0.5 0.6 mg) and seagrass (0.5 4 mg) samples were determined at the Department of Geological Sciences, University of Florida, Gainesville, FL using a ECS 4010 elemental analyzer (Costech) interfac ed via a ConFlo III to a Delta Plus XL isotope ratio mass spectrometer ( ThermoFisher Scientific ). Delta notation was used to express stable isotope abundances, defined as parts per

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60 (3 1) where R sample and R standard are the corresponding ratios of heavy to light isotopes ( 13 C/ 12 C and 15 N/ 14 N) in the sample and international standard, respectively. Vienna Pee Dee Belem nite was used as the standard for 13 C and atmospheric N 2 for 15 N. The reference material USGS40 (L glutamic acid) was used to normalize all results The standard deviation of the reference material was 0.13 both 13 C and 15 N values ( n = 58 ). Repea ted measurements of a laboratory reference material, loggerhead scute was used to examine consistency in a homogeneous sample with similar isotopic composition to the epidermis samples. The standard deviation of this laboratory reference material was 0.1 2 13 C values and 0.18 15 N values ( n = 21 ). Nitrogen isotopic composition of amino acids was analyzed for four T. testudinum samples collected in southern Nicaragua and one T. testudinum sample collected in Inagua, Bahamas, and for six green tur tles nesting at Tortuguero, using a sub sample of the epidermis sample used for bulk tissue analysis. Green turtle samples selected for AA CSIA represent the range of bulk epidermis 15 N values observed in the nesting population (solid triangles Fig ure 3 2A) Approximately 5 mg (5.0 5.4 mg) of homogenized turtle tissue and 30 mg (30.2 33.8 mg) of seagrass was hydrolyzed using 6N hydrochloric acid and then deri vatized to produce trifluoroacetic amino acid esters using methods previously described (Macko et al. 1997, Popp et al. 2007) N itrogen isotopic composition s of individual amino acids were determined using a Delta V Plus mass spectrometer (ThermoFisher Scientific) interfaced with a Trace GC gas chromatograph (ThermoFisher Scientific) as de scribed by Dale et al. (2011) and Hannides et al. (2009) Internal reference materials, norleucine and aminoadipic acid,

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61 were used to normalize m easured 15 N values. Each sample was analyzed in trip licate, and data are presented as the mean of three analyses. Standard deviations for all amino acids av 0. 2 1 1 Turtle Trophic P osition The fractional trophic positions (TP) of green turtles were based on the relationship between (SrcAA) amino acids using two variations of the equation proposed by Chikaraishi et al. (2009) First turtle TP was calculated using nitrogen isotopic comp osition s of glutamic acid ( glu ) and phenylalanine ( phe ) respectively : (3 2) where glu phe is the difference between glu and phe in the primary producer, which has been shown consistently to be for aquatic p rimary producer s (Chikaraishi et al. 2009, 2010) H owever, here I demonstrate that seagrass amino acid biosynthesis is more similar to that of C 3 plants rather than macroalgae and phytoplankton ( see Results glu phe (C 3 plants; Chikaraishi et al. 2010) was used to calculate turtle TP glu/phe The trophic enrichment factor (TEF) is the expected enrichment in 15 N with each trophic step for TrAAs and S rcAAs (Chikaraishi et al. 2010) a s calculated by: (3 3) C ontrolled feeding studies using herbivorous zooplankton and young carnivorous fish have c onsistent ly yielded TEF glu phe (Chikaraishi et al. 2009) and here this value was used to calculate turtle TP glu p he

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62 Second, turtle TP was calculated using a combination of all available TrAAs and SrcAA s : 3 4 where 15 N Tr is the weighted mean 15 N value of turtle TrAAs alanine (ala), leucine (leu), aspartic acid (asp) and glutamic acid (glu), and 15 N Src is the weighted mean of SrcAAs phenylalanine (phe), se rine (ser), glycine (gly), tyrosine (tyr), and lysine (lys). These amino acid classifications are based on McClellend and Montoya (2002) Popp et al. (2007) and Sherwood et al. (2011) Valine, isoleucine and threonine were omitted from this analysis because the y were not measured in all turtle samples, and proline was not reported because it co eluted with another compound in the turtle samples Arginine was measured in both green turtles and seagrass, but there is no published or TEF values, so it was not included in TP Tr/Src calculations. For the purposes of Eq uation 4 4 Tr Src was calculated as the difference between weighted means for 15 N Tr and 15 N Src in seagrass (this study) and C 3 plants (Chikaraishi et al. 2010) combined to yield Tr Src = 1.4 1.8 Asp, lys and tyr data were only available for seagrass, but given the consistent relationship observed in seagrass and C 3 plant amino acid nitrogen isotopic composition ( Figure 3 3 ) th ese amino acids were included in the calculation of the Tr Src value The TEF was determined using a weighted mean of TrAA and SrcAA data from the literature. TEF values for ala, leu, glu, gly, ser, and phe were mean values derived from multiple studies (Chikaraishi et al. 2010) and TEF values for asp, lys and ty r were derived fr om feeding study results (McClelland & Montoya 2002) to yield TEF Tr Src = 4.2 0.2 In

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63 both forms of the TP calculations the error associated with each component of the equation was propagated to determine TP e rror (Dale et al. 2011) Data A nalysis All statistics were performe d using R (R Development Core Team 2011) Isotope niche metrics (convex hull area and Bayesian ellipse area) were calculated using SIAR (Jackson et al. 2011) Convex hull area is the total area encompassed by all points on a 13 C 15 N bi plot (Layman et al. 2007a) but this method is particularly sensitive to sample sizes less than 50 (Jackson et al. 2011) Because the convex hull area is based on the outer most points to construct the polygon, extreme values or outliers can heavily influence the resulting are a. The ellipse area was proposed as a metric that is unbiased with respect to sample size, and, particularly for the Bayesian method incorporates greater uncertainty with smaller sample sizes, resulting in larger ellipse areas (Jackson et al. 2011) The convex hull approach includes information about every part of the isotopic niche space, while the Bayesian approach is targeted at niche widths of (Layman et al. 2012) I provide area estimates using both metrics but I only plot convex hulls because I a m interested in extreme values in both the nest ing and foraging populations Results The nesting population at Tortuguero exhibited a wide range in bulk epidermis 13 C and 15 N values (Figure 3 2 A ). E stimates of Bayesian ellipse area for each foraging site result ed in slightly different size ranking s than the convex hull area method but both measurements of the isotopic niche convex hull area and Bayesian

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64 ellipse area generated smaller niche areas for each foraging aggregation than for the nesting population (Table 3 1, Figure 3 2 B ). Seagrass sampl es obtained from three foraging sites had a wide range of bulk 13 C and 15 N values (Table 3 2, Figure 3 2 C ). The range in 15 sites was larger than that in 13 3 2). Mean nitrogen isotope values in seagrass of Florida > Nicaragua > The Bahamas, and a similar trend was evident in the respective green turtle foraging aggregation means (Tables 3 1 and 3 2). Even wider ranges in isotope values of T. testudinum were observed when additional sites in the Caribbean we re included from the literature ( 13 15 N Table 3 3, Fig ure 3 2 C ). These ranges in the primary producer across the Caribbean were nearly as large as those of the Tortuguero nesting population. Seagrass nitrogen isotope f ractionation patterns in amino acids were found to be more similar to that of terrestrial C 3 plants than to other aquatic primary producers such as macroalgae, phytoplankton and cyanobacteria (Fig ure 3 3 ). TP glu/phe (calculated using only glu and phe) ra nged from 1.7 to 2.1 value for C 3 plants was used (Table 3 4 ; Fig ure 3 4 ). TP Tr/Src ( calculated using weighted means of all available TrAAs and SrcAAs) yielded similar results to that of TP glu/phe but with greater variability (Table 3 4 ; Fig ur e 3 4 ). The 15 N values of bulk epidermis and phe, a SrcAA showed a significant positive relationship (Fig ure 3 5 ). Two of the green turtles that were analyzed for AA CSIA were also satellite tracked. Those females migrated to northern Nicaragua (RAAN) to areas of seagrass habitat before their transmissions ceased (Sea Turtle Conservancy 2012). Their bulk epidermis 15 N values fall inside the RAAN isotopic niche, and AA CSIA data indicate

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65 herbivorous feeding. Therefore, interpretations made from isotop ic data for these individuals were consistent with satellite tracking information. Discussion Interpreting the Isotopic N iche Multiple lines of evidence indicate that the Tortuguero green turtle population is composed of herbivores that feed over a wide ge ographic range of neritic habitats with differences in stable isotope compositions of the primary producers. The wide range in bulk 13 C and 15 N values of the nesting population can be explained by distinct isotopic niches at foraging grounds, thus suppo rting geographic, or scenopoetic, differences as a primary cause for isotopic variation among nesting turtles. This is consistent with the prediction of Bearhop et al. (2004) that populations foraging across a range of geographical areas are likely to show more variation in stable isotope values than those from sedentary populations. The n esting population represents a mixture of individuals from a wide range of foraging sites, whereas each foraging aggregation is comprised of individuals that are relatively site fixed (Lohmann et al. 1997, Campbell 2003, Bjorndal et al. 2005, Mey lan et al. 2011) The large variation in bulk stable isotope values of T. testudinum across the Greater Caribbean and the results of AA CSIA provide strong evidence that baseline differences are the main contributor to variation in the bulk epidermis 15 N values of turtles from the nesting population. Despite a wide range of 15 N values t he close relationship between turtle bulk epidermis 15 N and phe 15 N values and similar TP glu/phe estimates indicate that turtles are feeding at a similar trophic posi tion across their geographic range. C ombin ing all available SrcAA and TrAA data to calculate TP Tr/Src

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66 demonstrate s similar (though more variable ) result s I conclude TP is most robustly estimated by comparing nitrogen isotopic composition s of glu and phe in concurrence with results of Chikaraishi et al. (2009) I value appropriate for seagrass is equal to that of C 3 terrestrial plants and very different from other aquatic primary producers. This is not surprising, given s eagrass es are descendant from terrestrial angiosperms and use a C 3 pathway of photosynthesis (Hemminga & Mateo 1996, Waycott et al. 2006) Trophic position estimates were meaningless value measured for other aquatic food webs, which underscores the need to understand amino acid metabolism in the primary producer at the base of the food web when calculating TP based on amino acid nitrogen isotopic composition. It is also necessary to understand habitat derived differences in stable isotope patterns when translating the isotopic niche to the ecological niche (Flaherty & Ben David 2010) The observed range in 15 N could represent two or more trophic levels (Post 2002) using available nitrogen isotope discrimination factors for epidermis in green turtles (2.8 Chapter 2 ) In addition, lower 13 C values a re found in oceanic / pelagic habitats compared to coastal / benthic habitats (Rubenstein & Hobson 2004) A subset of the nesting population exhibits the combination of low 13 C value s and high 15 N values that could be indicative of an oceanic, carnivorous feeding strategy as displayed by juvenile green turtles and loggerheads, Caretta caretta (Reich et al. 2007, Pajuelo et al. 2010) A carnivorous portion of the Tortuguero nesting population would align with the carnivorous foraging patterns of some Pacific populations determined through stable

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67 isotope analysis, stomach contents, satellite tracking, and vi deo analysis (Bjorndal 1997a, Hatase et al. 2006, Amorocho & Reina 2007, Arthur et al. 2007, Lemons et al. 2011) Yet neither the AA CSIA based trophic position estimates or additional investigation into the isotopic niche provide evidence of carnivory, thus underscoring the ecological role o f the Atlantic green turtle as a primary consumer (reviewed in Bjorndal 1997) These turtles do not exhibit evidence of alternative foraging strategies, likely as a consequence of the extensive seagrass pastures found throughout the Caribbean and a popula tion size that is far from carrying capacity (Bjorndal & Jackson 2003) Assessing the isoto pic niche of the nesting population without the additional information used in this study might have led to incorrect interpretations. Therefore, I emphasize that caution must be used when interpreting the isotopic niche of wide ranging consumers to avoid erroneous conclusions when scenopoetic differences cause isotopic variation across the foraging range. Assessing P opulation C onnectivity with the I sotopic N iche Extensive commercial take of large juvenile and adult green turtles occurs in Nicaragua (app roximately 7,000 to more than 11,000 taken annually from the mid 1990s to the present), and to a lesser extent, in other areas throughout the Caribbean (Lagueux 1998, Campbell 2003, Brautigam & Eckert 2006, Lagueux and Campbell, unpubl. data) Seagrass beds in the coastal waters of Nicaragua are known to host foraging individuals that nest in Tortugue ro (Trong et al. 2005) but estimates of the proportion of this nesting population that come from foraging grounds in Nicarag ua vary. Understanding population interconnectivity is important for population modeling and management, particularly in Nicaragua, where green turtle take is extremely high.

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68 I explore the isotopic niche as an alternative method to assess the contributi on of Nicaragua foraging areas to the Tortuguero nesting population. If both northern (RAAN) and southern (RAAS) Nicaraguan foraging aggregations are combined in a single convex hull, the resulting isotopic niche contains 66% of the Tortuguero samples in this study. This estimate is a maximum contribution, as it is possible that some turtles could also migrate to the nesting site from other areas with similar carbon and nitroge n isotopic characteristics (Figure 3 2 B ). Other methods have been used to est imate the Nicaragua contribution to the Tortuguero nesting population. Flipper tag recoveries from over 4,600 individuals have indicated that Nicaragua hosts between 82 and 86% of the Tortuguero nesting population (Carr et al. 1978, Trong et al. 2005) The disadvantage of this classical method is that data are acquired primarily through fisheries tag returns, and are thus biased by capture effort. Satellite tracking demonstrated that of the 15 green turtles tracked from Tortuguero since 2000, 80% have returned to Nicaraguan fo raging grounds (Sea Turtle Conservan cy 2012) However, the expense of this technology often limits sample size. Finally, genetic mixed stock analysis does not face the same drawbacks as satellite telemetry or tag returns. This method, using a many to many approach, estimates the Nicaragua contribution at 65% with a range between 33 88% (Bolker et al. 2007) My estimate using the isotopic niche is smaller than the estimat ed contribution from Nicaragua through tag returns and satellite telemetry, but is closer to that of the genetic mixed stock approach. The isotopic niche provides a complementary method to evaluate foraging ground contributions to the nesting population a nd may be useful

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69 when isotopic variation exists among foraging grounds, as long as the source of isotopic variability (i.e., bionomic vs. scenopoetic) is understood. Additional information on bulk epidermis and compound specific amino acid isotope values of green turtles at other foraging grounds would improve our ability to assess population connectivity using isotopic niches. Outcome s The Tortuguero nesting population is herbivorous and feeds over a wide geographic range as indicated by seagrass stable i sotope composition and AA CSIA of amino acids. I demonstrate that the range in stable isotope values of the Tortuguero nesting population is primarily determined by scenopoetic factors rather than bionomic factors. Whereas 15 N values are typically used as a bionomic indicator of trophic level, I found that scenopoetic differences as a result of spatial variation in the primary producer greatly influence green turtle 15 N values. Therefore, I caution that bulk tissue stable isotope values of a highly dis persed or wide ranging species may be difficult to interpret in the absence of baseline values or without the use of AA CSIA to understand causes of isotopic variability. Information on where and what green turtles eat is critical to protecting the areas in which these turtles spend the majority of their lives and for assessing the risk of encountering anthropogenic threats such as oil spills or incidental capture in fisheries.

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70 Table 3 1. Number of green turtles, size range, and year sampled at each of t he five foraging grounds and the nesting beach location (Tortuguero). Site name Country Number of individuals sampled Size range CCL (cm) Year sampled Convex h ull a rea Bayesian ellipse a rea Mean 13 (min, max) Mean 15 (min, max) Inagua Bahama s 62 38.9 65.5 2008, 2009 18.5 4.0 6.35 ( 8.84, 4.48) 1.68 ( 1.94, 5.05) Long Island Bahamas 9 30.8 44.8 2010 7.5 6.1 9.43 ( 12.16, 6.36) 5.15 (3.50, 7.10) RAAN Nicaragua 110 85.0 106.6 2010 20.3 2.7 8.99 ( 14.69, 7.26) 5.56 (3.13, 7.86) RAA S Nicaragua 73 69.5 106.0 2009 2011 10.7 1.8 10.02 ( 13.01, 8.24) 6.55 (4.24, 7.89) St. Joe Bay, Florida USA 20 31.7 60.5 2010 13.7 5.3 12.26 ( 15.65, 9.04) 8.14 (4.86, 11.11) Tortuguero Beach Costa Rica 102 93.7 122.1 2007, 2009 44.9 8.4 9.31 ( 16.98, 5.26) 6.58 (3.02, 9.35) Two estimates of isotopic niche area (convex hull area and Bayesian ellipse area) were calculated for each site as well as isotopic means and minimum/maximum values. CCL is curved carapace length.

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71 Table 3 2. Seagrass ( Thalassia testudinum ) carbon and nitrogen isotope compositions provided as mean and minimum/maximum values. Location Country Number of sites Year sampled Mean 13 (min, max) Mean 15 (min, max) Union Creek, Great Inagua Bahamas 3 2002 6.6 4 ( 7.13, 6.37) 1.17 (0.39, 1.75) Pearl Cays, RAAS Nicaragua 4 2010 8.97 ( 10.35, 7.80) 3.19 (2.57, 4.27) St. Joe Bay, Florida USA 1 2011 7.72 5.57 One sample was collected at each site.

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72 Table 3 3. M ean and SE of 13 C and 15 N values of Thalassia testudium analyzed in this study and collected from the literature for sites in the Greater Caribbean. Site ID 13 C SE 13 C 15 N SE 15 N n Location Source 1 6.64 0.12 1.17 0.2 9 Union Creek, Inagua, Bahamas This study 2 9.37 0.66 2.94 0.46 4 RAAS, Nicaragua This study 3 7.72 0.06 5.57 0.01 2 St. Joe Bay, Florida, USA This study 4 6.34 0.06 2.97 0.12 2 Tobacco Reef, Belize Barrier Reef, Belize (Abed Navandi & Dworschak 2005) 5 7.2 0.27 2.08 0 .41 12 (Anderson & Fourqurean 2003) 6 8.44 0.33 2.15 0.19 12 (Anderson & Fourqurean 2003) 7 10.41 0.38 1.14 0.2 12 (Anderson & Fourqurean 2003) 8 7.7 0.16 1.7 0.24 12 Florida Keys N (Anderson & Fourqurean 2003) 9 7.5 0.35 3.2 0.35 2 Florida Keys, ocean side, USA (Behringer & Butler 2006) 10 6.9 0.21 2.9 0.14 2 Florida Keys, impacted bay side, USA (Behringer & Butler 2006) 11 6.5 0.49 2.8 0.42 2 Florida Keys, non impacted bay side, USA (Behringer & Butler 2006) 12 10.7 0.2 6 0.3 10 Florida Bay, USA (Fourqurean & Schrlau 2003) 13 14.1 3.2 Laguna Joyuda, Puerto Rico (France 1998) 14 13.6 3.7 1 Schooner Bank, Florida Bay, USA (Harrigan et al. 1989) 15 11.5 1.62 1.4 0.14 3 Biscayne Bay, Florid a, USA (Kieckbusch et al. 2004) 16 8 0.58 0.2 0.46 3 Andros & Grand Bahamas Island, Bahamas (Kieckbusch et al. 2004) 17 8.5 0.3 0.3 0 4 Jaragua, Dominican Republic (Tewfik et al. 2005) 18 8.7 0.1 3.1 0.1 4 Barahona, Dominican Republic (Tewfik et al. 2005) 19 7.3 0.29 2.6 1.04 3 Twin Cays Belize (Wooller et al. 2003) If the standard error was not reported in the original study, it was calculated using the reported standard deviation and sample size. Site ID refers to the identification numbers in Figure 3 2C.

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73 Table 3 4 Bulk tissue and amino acid 15 N values of Tortuguero green turtle epidermis and seagrass ( Thalassia testudinum ) Green turtles Seagrass 86070 99251 104857 113826 WCC SC MC LR UC Bulk 3.3 3.0 9.4 7.8 5.4 6.0 2.6 2.7 4.3 2.2 0.4 Trophic Glu* 6.7 5.5 13.3 11.5 7.9 9.0 4.7 4.7 5.5 4.0 2.6 Ala* 8.4 9.4 14.3 10.6 11.2 11.0 3.8 5.7 4.3 Asp* 6.3 4.4 11.7 9.1 8.2 10.2 4.7 6.5 6.2 4.9 3.9 Leu* 6.6 5.1 13.4 9.1 8.8 8.7 1.5 3.2 3.3 2.5 0.1 Ile 8.5 4.2 12.9 9.7 0.3 5.2 2.3 Va l 3.6 3.6 12.2 8.6 6.4 7.3 5.2 Source Phe* 9.7 7.3 13.1 12.0 10.0 9.5 13.0 13.4 12.9 12.7 9.2 Gly* 6.9 6.9 15.1 10.5 10.0 10.0 2.9 1.5 0.3 5.1 Lys* 0.4 1.4 4.2 1.4 0.3 1.1 3.0 3.0 0.9 Ser* 3.3 5.0 12 .0 10.3 6.6 8.6 3.6 0.3 1.2 2.1 3.8 Tyr* 1.8 0.1 4.4 0.3 1.0 0.4 3.0 3.0 0.9 Arg 1.1 11.8 0.7 1.5 2.4 1.1 3.4 2.4 0.5 2.4 6.6 Thr 3.7 3.2 1.7 0.1 0.1 0.8 4.6 3.5 2.6

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74 Table 3 4 Continued. Green turtles Seagrass 86070 9 9251 104857 113826 WCC SC MC LR UC TP glu/phe 1.7 1.9 2.1 2.0 1.8 2.0 TP Tr/Src 1.4 1.6 1.0 2.6 1.4 1.9 Green turtles are identified by their flipper tag numbers. Seagrass sampling sites include four sites within th e Pearl Cays, Nicaragua (WC=Wild Cane Cay, SC=Savanna Cay, MC=Maroon Cay, LR=Long Reef), and one in The Bahamas (UC=Union Creek). Trophic amino acids: Glu = glutamic acid, Ala = alanine, Asp = aspartic acid, Leu = leucine, Ile = isoleucine, Val = valine. Source amino acids: Phe = phenylalanine, Gly = glycine, Lys = lysine, Ser = serine, Tyr = tyrosine, Arg = arginine, Thr = threonine. Trophic position (TP) was calculated in two ways (see Methods): using only glu and phe (TP glu/phe ) or a combination of se Tr/Src ). *amino acids used in trophic position calculations

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75 Figure 3 1. Map of five foraging grounds (circles) and one nesting beach (star) where green turtles were sampled. T halassia testudinum samples were collected at the three foraging grounds with open circles. This figure was created with Seaturtle.org Maptool (http://www.seaturtle.org/maptool/).

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76 Figure 3 2. Bulk tissue 13 C and 15 N values in green turtles and seagrass. A) G reen turtle epidermis from the nesting population at Tortuguero, Costa Rica. Solid symbols represent the six epidermis samples that were used for AA CSIA. Circles around two of the solid symbols identify the two individuals that were a lso satellite tracked. B) G reen turtle epidermis at five foraging sites and one nesting beach (Tortuguero). Convex hulls represent the isotopic niche area for each population. C) S eagrass ( Thalassia testudinum ) samples from three green turtle foraging s ites (1 = Inagua, Bahamas, 2 = RAAS, Nicaragua, 3 = Florida, USA) in this study as well as 15 other sites around the Greater Caribbean. Points are means SE except for 13 and 14, for which SEs were not available. See Table 3 3 for complete list of sites and sources. A B C c

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77 Figure 3 3. Difference in 15 N values between each amino acid and phenylalanine 15 N AA Phe ) for Thalassia testudinum seagrass (this study), terrestrial C 3 plants (Chikaraishi et al. 2010), and 25 aquatic primary producers (Chikaraishi et al. 2009) T. testudinum amino acid profile is more similar to that of C 3 plants than other aquatic primary producers. The relationship between seagrass and C 3 15 N AA Phe values is significant for the eight AAs for which data are available in both groups 15 N AA Phe (seagrass) 15 N AA Phe (C3) 0.90(0.11) + 1.3(1.4); r 2 = 0.91)

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78 Figure 3 4. Trophic position was calculated for each turtle using the nitrogen isotope composition of amino acids through two approaches: with glutamic acid (glu) and phenylalanine (phe) (TP glu/ph e Tr/Src ) (see Methods). The horizontal line at 2 indicates the expected trophic position of an herbivore. Bars indicate 1 SD.

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79 Figure 3 5. The relationship between the bulk epiderm is 15 N and phe 15 N values in green turtle epidermis is significant (n = 6, F = 19.3, r 2 = 0.78, p = 0.012).

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80 CHAPTER 4 INDIV IDUAL SPECIALISTS IN A GENERALIST POPULATION: RESULTS FROM A LONG TERM STABLE ISOTOPE SERIES Introduction (1957) con ceptualization of the niche as an n dimensional hypervolume of resource use has since been expanded in the ecological literature. Van Valen (1965) first incorporated the idea of individual variation in resource use into niche theory, but intra population variation in resource use is often overlooked in ecological studies (Bo lnick et al. 2003) Though there are many niche concepts based on various ecological characteristics, a recent expansion of niche theory uses stable isotopes as the measure of niche width (Bearhop et al. 2004, News ome et al. 2007) Examining intra and inter individual isotopic variance can be an effective way to investigate specialization and the ecological niche (Newsome et al. 2007) Stable isotopes of consumers reflect that of prey as well as the habitat of the individual. Nitrogen isotopes typically indicate trophic position (DeNiro & Epstein 1981, Post 2002) whereas carbon isotopes reflect variation in baseline producers or habitat (DeNiro & Epstein 1978) Tissues that are created over time and remain inert after synthesis, such as hair, otoliths, and baleen, reflect resource use at the time of formation (Hobson 1999) and allow longitudinal sampling with stable isotope analysis of successive microlayers (Cerling et al. 2009, Cherel et al. 2009) Sea turtles have such a tissue, scute, which is keratinized epidermis covering the bony shell of most chelonians. Scute grows from basal epidermis and accumulates with the oldest tissue at the surface, making possible the examination of resource use (which is define d here as the integration of diet, habitat, and geographic location) of individuals over time.

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81 Figure 4 1 presents a conceptual model of the isotopic records from an inert tissue of three hypothetical time series of resource use for one specialist and two generalist populations. In this model, stable isotope values may be influenced by diet, habitat type, and geographic location. I use specialization to refer to the use of a relatively limited fraction of the possible range of available resources. In the specialist population ( Fig ure 4 1 A ), both individual and population isotopic niche widths are narrow. In the first generalist population ( Fig ure 4 1B ), generalist individuals vary widely in their resource use, resulting in an isotopic record that shifts through time so that both individuals and the population occupy a wid e isotopic niche space. In the second generalist population ( Fig ure 4 1 C ), specialist individuals maintain consistent resource use within a narrow isotopic niche space, but variation among individuals results in a wide population isotopic niche. Without long term individual records, the generalist populations in Figures 4 1B and 4 1C are indistinguishable. As drawn, the conceptual model assumes no temporal variation. However, the horizontal lines in F igures 4 1 A and 4 1 C would exhibit a cyclic pattern i f seasonal variation occurred. This model does not address asynchronous temporal variation among sites. The endangered loggerhead sea turtle ( Caretta caretta ) is a generalist species that feeds on a wide range of prey (Bjorndal 1997a) Loggerheads nesting in Florida forage over a broad geographic range from New Jersey, USA, to Belize, and these geographic areas have different isotopic ba selines (Reich et al. 2010, Pajuelo et al. 2012) I examine long term consistency in resource use of a nesting loggerhead p opulation through stable isotope analysis of nitrogen and carbon in scute layers to distinguish between the two types of generalist populations. Given the generalist nature

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82 at the population level, my objective is to reveal the individual patterns of reso urce use in loggerheads to determine if the population is composed of individual specialists or generalists. Materials and Methods Scute Sampling and A nalysis Scute samples were taken with sterile 6 mm biopsy punches from 15 adult female loggerheads (cur ved carapace length range 86.5 108.8 cm) while nesting at Cape Canaveral National Seashore, Florida, USA, in May June 2004. Two scute biopsies were taken from each turtle on opposite corners of the third lateral scute of each individual: one in the poster ior margin near the central scute and the other at the opposite anterior corner along the border with the marginal scutes (see Reich et al. 2007) Of the two scute samples taken from each individual, the longer sequence was used for stable isotope analysis. Minimum curved carapace length of each female was measured from the a nterior point at midline to the posterior not ch at midline between the supracaudal scutes (Bolten 1999) Scutes were preserved in 70% ethanol after collection for approximately the sa me time period, and each sample was rinsed clean in deionized water before drying at 60C for 24 hours. After lipid extraction with petroleum ether using an accelerated solvent extractor, scutes were microsampled in 50 le for stable isotope analysis using a carbide end mill with x, y, and z axis controls to a Samples of 500 n ECS 4010 elemental analyzer (Costech) interfaced via a ConFlo III device to a DeltaPlus XL isotope ratio mass spectrometer (ThermoFisher Scientific) in the Department of

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83 Geological Sciences at the University of Florida, Gainesville, Florida. Delta ( ) notation is used to ex press all stable isotope ratios relative to the standard in parts per thousand (4 1) where R sample and R standard are the corresponding ratios of heavy to light isotopes ( 13 C/ 12 C and 15 N/ 14 N) in the sample and international standard, respectively. Standards were Vienna Pee Dee Belemnite (VP DB) for 13 C and atmospheric N 2 for 15 N. The reference material USGS40 (L glutamic acid) was used as a calibration standard in all runs The standard deviation of the reference material was 13 C and 15 N, respectively (n = 37). Repeated m easurements of a laboratory reference material, loggerhead scute, was used to indicate analytical precision of the measurements in a homogeneous sample with similar isotopic composition to the unknown samples. The standard deviation for this laboratory re ference material was 13 C and 15 N, respectively (n = 18). One anomalous layer (out of 196) was excluded from a nalysis because in sufficient sample was available to reanalyze it. The excluded point is indicated by the dashed line in F igu re 4 2 V ariation in 15 N and 13 C was analyzed test. P rotected ANOVAs were used to compare variation in 15 N or 13 C within and among turtles. Statistical analyses were performed with S Plus softwa re (version 8.1; TIB CO Spotfire Estimation of S cute A ge I represented in an entire scute sample. Scute turnover was estimated in four steps from

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84 the carbon incorporat ion rate measured in juvenile loggerheads. This rate was adjusted to non growing adults of a larger body mass and was applied to a shift in resource use in the scute record of a single individual growth. Step 1 : Isotopic incorporation rate in juvenile loggerheads excluding growth The fractional rate of isotopic incorporation ( k st ) describes the daily isotopic change in a tissue, which Hesslein et al. (Hesslein et al. 1993) demonstrated is the sum of the growth rate of the tissue ( k gt ) and the rate of catabolic degradation ( k dt ). k st = k gt + k dt T he isotopic incorporation was attributed to catabolic degradation alone by setting k st = k dt as growth in mature loggerheads is negligible (Bjorndal et al. 1983) Reich et al. (2008) report the catabolic degradation component of turnover for juvenile turtles as k dt = 0.013 day 1 for carbon. Step 2: Scaling to adult body mass using 1 / 4 power There is a two ord ers of magnitude difference in mass between adult and juvenile loggerheads: 1.7 kg for juveniles (Reich et al. 2008) whereas adult loggerheads are approximately 115 kg (Dodd 1988) The fractional rate of turnover is thought to be allometrically related to body mass as a result of whole body protein turnover rates and the rate of elemental incorporation into a tissue (Martnez del Rio et al. 2009b) There is evidence that this turnover rate scales with body mass to the 1 / 4 power (Carleton & Martnez del Rio 2005, Bauchinger & McWilliams 2009) Therefore, the value of catabolic tu rnover ( k dt ) for carbon measured in juvenile loggerheads (Reich et al. 2008) was estimated for adult turtles by using a 1 / 4 power body mass scaling to yield k dt = 0.0045.

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85 M ass 1 = 1.7 kg k dt1 = 0.013 day 1 Mass 2 = 115 kg k dt2 = 0.0045 day 1 Step 3: Turnover after four half lives Using the adjusted incorporation rate, complete turnover was estimated as four half lives (Seminoff et al. 2007) which is the time a new isotopic equilibrium would be reached after a shift in resource use. One half life was estimated by using ln(2)/ k st and after four half lives, tur nover is 93.75% complete. Turnover = 4 ln(2)/0.0045 day 1 = 1.7 yr Step 4: Turnover applied to resource use shift example The turnover time was applied to an apparent shift in the 13 C values of one individual that occurred over several layers ( Figur e 4 3 ). In that resource shift, t urnover is achieved after three layers. If it is assume d that layer is equivalent to 0.6 years (1.7 yr to turnover divided by 3 layers for linear scute growth). The scute records in this study range from 400 to 1100 the entire scute record ranges from approximately 4 to 12 years (median 8). No data are available on sea turtle scute growth rates or retention time to make precise estimates of the time period represented in these samples. The scute record does not extend throughout the lifetime of the animal, except in young turtles, as scute is subject to gradual mechanical wear. Whereas superficial layers may be worn away on loggerheads, the persistence of epibionts indicates that scute may be present for several years (Day et al. 2005) The time estimates we re calculated from an allometric relationship between isotopic turnover and body mass that has been demonstrated in endotherms (Bauchinger & McWilliams 2 009) Because these methods do not account

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86 for differences in temperature, it is possible that the turnover time in these ectotherms is an underestimate (Gillooly et al. 2001) Results O ne 50 was estimated to represent 0.6 years. The entire scute record ranges from 4 to 12 years (median 8). Individuals exhibit high consiste ncy in both 15 N and 13 C ( Figure 4 2 ), and the mean range of individuals is much smaller than that of the population for nitrogen and carbon ( T able 4 1 ). Individual patterns in resource use in both 15 N and 13 C combined ( Fig ure 4 4 ) reveal individual co nsistency (MANOVA, F = 437, p<0.001). Based on ANOVAs, variation within individuals (<7% of total variation) was less than that among individuals ( T able 4 2 ). Discussion L oggerhead scute samples may contain up to 12 years of resource use history, provid ing a lengthy record from which to investigate patterns in a long lived species. T o my knowledge, this study reports the longest record of resource use history obtained from living individuals. Comparison of l ong term scute records ( Fig ure 4 2 ) with i soto pic scenarios in the conceptual model ( F igure 4 1 ) reveal s this generalist population is composed of individual specialists. Though all of these loggerheads were sampled at the same nesting beach and an entire ocean basin is potentially available to the p opulation in which to forage individuals utilize only a limited fraction of the available isotopic niche space ( Figure 4 4 ).

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87 In this study, specialization is not limited to a diet consisting of a single prey item but the observed isotopic specialization result s from a consistent mixture of prey habitat and geographic location, which cannot be separate d with the sampling regime used Consumption of a prey mixture is likely, as individual loggerhead stomach contents often contain several prey species (Bjorndal 1997a) Whereas some of the variation among individuals may be a consequence of individual variation in isotopic discrimination or physiology rather than differences in f oraging (Barnes et al. 2008) it is unlikely this would result in the wide isotopic range observed. The large population range in 15 could be in dicative of a population that is feeding over several trophic levels if the nitrogen isotope values at the base of the food web are stable in all of the foraging locations of these individuals (Post 2002) However, if nitrogen isotope values at the base of the food web change with foraging location, isotopic differences will be more reflectiv e of habitat or location than of trophic level feeding differences because the same prey species will have different stable isotope values among these areas. I believe locational differences are more likely than trophic level differences, as the similarly large range of 13 in the nesting population indicates that loggerheads have geographically separated foraging areas and/or are incorporated in food webs with primary producers that are relatively enriched or depleted in 13 C The gap in 13 C values between 12.5 and Figure 4 2B ) represents the division between two foraging groups identified by Reich et al. (2010) T he groups could represent two general habitat use patterns that result from food webs with different 13 C values at the base of the food web as a consequence of an isotopic

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88 gradient (e.g. oceanic/neritic, pelagic/benthic, latitudinal). Only one turtle crossed between groups, indicating that individuals have high fidelity to foraging sites and/or habitat type. This foraging fidelity is consistent with the observations of eight adult female loggerheads tracked from North Carolina; two di fferent movement types were observed, but all individuals exhibited inter annual fidelity to discrete foraging sites (Hawkes et al. 2007) Intra population variation in resource use can have ecological, evolutionary, and conservation consequences. R esource use heterogeneity, indicated by the broad population isotopic niche width and narrow individual niche widths reduce s intraspecific competition and may alter selective pressures (Bolnick et al. 2003) Reduction in intraspecific competition appears to be substantial in adult loggerheads, given the small proportion of variance in this study attributed to wit hin individual variation (<7%, T able 4 1). In compari son, a recent study of individual specialization in sea otter s, based on stable isotope values of vibrissae estimated that 28% of the variance was attributed to within individual variation (Newsome et al. 2009) Examining the degree of in dividual specialization within a population provides a better understanding of its ecology, behaviour, and population dynamics. The approach used in this study to examine resource use in individuals and populations has broad application for species that p ossess consistently growing, inert tissues that can be serially sampled. Because diet and habitat are confounded in stable isotope values of consumers and could not be separated in this study, loggerheads should be sampled at a series of foraging grounds to distinguish the effects of diet, habitat, and geographic

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89 location and identify the major component contributing to the high degree of individual specialization that was observed

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90 Table 4 1 Minimum, maximum, and mean ranges of 15 N and 13 C for indi vidual scute records ( n = 15). M inimum M aximum M ean range P opulation 15 N 0.33 2.42 0.93 (0.66) 9.03 13 C 0.36 3.23 1.26 (0.65) 10.45 The population range is the difference between the maximum and minimum valu es for all individuals.

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91 Table 4 2. ANOVAs indicate significant differences b etween the means of individuals. SS among SS within F p value 15 N 1251.7 89.4 533.2 <0.001 13 C 1767.8 36.6 623.3 <0.001 A large proportion of the variation was a result of differences among rather than within individuals.

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92 Figure 4 1 Conceptual model of three population patterns of nitrogen stable isotope values representing resource use through time. Arrows track individuals, and e ach circle represents the 15 N valu e for a layer of inert tissue which reflects resource use (integration of diet, habitat, and geographic location) See text for discussion of the three strategies.

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93 Figure 4 2. Stable isotope values in successive layers of scute from 15 logger heads. A ) Nitrogen isotope values. B ) Carbon isotope values Each line represents all layers for one individual noted with a unique symbol. Starting points and intervals vary for some individuals because layers were combined to provide sufficient samp le for analysis. The number of layers reflects the thickness of the sample. A B

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94 Figure 4 3. Plot of one loggerhead scute record that was used to estimate the time period in which a shift in resource use occurred. As scute grows fro m the ventral surface up, the x axis represents youngest to oldest tissue from left to right on the graph. The solid arrow indicates where the shift begins and the dashed arrow indicates the equilibrium value when the shift is complete. This has been plotted using the same a xes as Figure 4 2 for ease of comparison.

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95 Figure 4 4. 13 C and 15 N biplot for sequence of scute layers. Symbols represent the same individuals as in Figure 4 2.

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96 CHAPTER 5 TEMPORAL CONSISTENCY AND INDIVIDUAL SPECIALIZATION IN RESOURCE USE BY GREEN TURTLES IN SUCCESSIV E LIFE STAGES Introduction Whereas most studies of resource use have focused on whole populations and treated all individuals as equal a closer look at the ecology of individuals has revealed increasing accounts of individual special ization (Bolnick et al. 2003, Arajo et al. 2011) This phenomenon, in which individuals u base, has been observed even among individuals of the same age and sex (Bolnick et al. 2003) Specialists represent one extreme along a continuum of intra population variation in resource use, whereas generalists individuals that use a broad range of resour ces represent the other extreme. M any studies that measured individual specialization however, did not provide a time frame. Determining the timescale over which niche variation persists is important because the temporal consistency of individual specia lization has implications for both ecology and evolution (Bolnick et al. 2003) In this study, I define temporal consistency as a measure of the mean individual variation in niche use through time. Individual specialization is relevant only to generalist populations, in which individuals have a substan tially reduced niche compared to that of the population. I define these terms to distinguish between two fundamentally different concepts that dr o ve this study. Stable isotope analysis is one strategy that has been employed to examine consistency in resou rce use, specialization, and trophic niche breadth (Jaeger et al. 201 0a, Codron et al. 2012, Fink et al. in press) Carbon and nitrogen stable isotope value s assimilated through the diet can reflect the ecological niche of a consumer, as

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97 the values are determined by trophic position and habitat use. Because both habitat and diet influence on stable isotope values, I the integration of these two factors in the foraging history of the animal. If stomach content analysis is used t o determine whether resource use is temporally consis tent, individuals must be sampled repeatedly through time. Alternatively, stable isotope analysis of tissues that remain inert after synthesis provide s a time series of resource use history. That is, a single tissue sample from an individual can be subsa mpled to pro vide a continuous chronological, making it unnecessary to re sample the organism on multiple occasions. These tissues are often composed of keratin for example the baleen of whales (Schell et al. 1989) the whiskers of marine mammals (Newsome et al. 2009) and scute s of sea turtle carapace s (Vander Zanden et al. 2010) I nter and intra individual isotopic variance can be used to characterize a population as one of three types. The conceptual model used in this study extends the population categories outlin ed by Bearhop et al. (2004) to include temporal consistency with predictions for repeated samples through time (Figure 5 1) I use an example with 15 N values though other measures representative of the ecological niche would also be appropriate. A spec ialist population occupies a narrow isotopic niche space, and individuals are consistent through time ( Figure 5 1A ). Generalist populations can be composed of generalist individuals (Type A) or specialist individuals (Type B) and display a wide range of i sotopic values Generalist individuals have low temporal consistency and high isotopic variance through time, with both individuals and the population reflecting a wide isotopic niche ( Figure 5 1B ). Specialist individuals maintain temporally consistent r esource use and display low intra individual isotopic variance

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98 High inter individual variation contributes to the wide population isotopic niche ( Figure 5 1C ). To differentiate among these population types, I use d a metabolically inert tissue that provi des a diachronic stable isotope record from a single sample. Long term diet information could also have be en obtained through repeated sampling of the same individual or by using tissues that have different turnover rates and integrate different time scal es (Bearhop et al. 2004, MacNeill et al. 2005, Martnez del Rio et al. 2009a, Matich et al. 2011) Resource use specialization within a single age class has been documented in a number of studies (compiled in Bolnick et al. 2003, Arajo et al. 2011) and stabl e isotope analysis has been used to investigate patterns of temporal consistency and individual specialization in marine organisms such as brown skuas (Anderson et al. 2009) sea otters (Newsome et al. 2009) loggerhead sea turtles (Vander Zanden et al. 2010) jumbo squid (Lorrain et al. 2011) and bull sharks (Matich et al. 2011) F ew studies however, have examined how temporal consistency and/or individual specialization changes across life stages within a single species (Nshombo 1994, Sword & Dopman 1999, Frdrich et al. 2010) Individuals may vary with respect to the degree of consistency and specialization in resource use at different ages, particularly if ontogenetic diet shifts occur. The focal species of this study was the Caribbean green turtle ( Chelonia mydas ), which undergoes ontogenetic changes in foraging patterns. Prior to recruiting to coastal waters in the Caribbean, young juvenile gre en turtles use oceanic, or open ocean, hab because of the lack of knowledge regarding diet and location (Carr 1987) Oceanic juveniles are o mnivorous

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99 or carnivorous and are believed to forage opportunistically until they recruit to neritic, or coastal, habitats between three and six years of age (Bjorndal 1997a, Zug & Gl or 1998, Reich et al. 2007) The carapace length of green turtles in the western Atlantic is approximately 20 25 cm when they arrive in the neritic habitat (Bjorndal & Bolten 1988) at which point they shift to an herbivorous diet and feed in shallow waters (Bjorndal 1997a, Reich et al. 2007) Whereas this shift has been observed to be rapid among young green turtles in the western Atlantic (Reich et al. 2007) it may occur more gradually, as in the case of juvenile green turtles from the NW coast of Afr ica (Cardona et al. 2009) As green turtles age and remain in coastal foraging grounds in the Caribbean, they often shift to deeper waters (Bresette et al. 2010) but few diet studies of large juvenile or adult turtles at their foraging grounds in the western Atlantic (Mortimer 1981) Past analyses of stomach contents reveal that the seagrass Thalassia testudinum is the primary species in the diet of neritic green turtles, though t hey may also feed on algae and occasionally on animal matter (Bjorndal 1980, 1990, Mortimer 1981) A disadvantage of stomach content analysis is that it provides only a snapshot in time of the feeding patterns of an individual. In this study, I add ress ed two objectives by comparison of green tu rtles in successive life stages, i.e. oceanic juveniles, neritic juveniles, and adults. First, I quantif ied the temporal consistency in resource use of individuals through time for each life stage. Second, I evaluated individual specialization in resource use at each life stage to determine the proportion of the total population niche used by individuals. Juveniles in the open ocean are thought to range over large areas and be opportunistic consumers (Bolten 2003) ; thus, it was expected that during the oceanic portion of their

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100 foraging history green turtles would have less consistent isotopic values and that the population would be composed of generalist individuals. Neritic green turtles display high fidelity to foraging areas (Lohmann et al. 1997, Campbell 2003, Bjorndal et al. 2005, Meylan et al. 2011) and may maintain a consistent diet through t ime (Burkholder et al. 2011) Therefore, neritic juvenile and adult green turtles were expected to exhibit high temporal consistency in resource use. It was also expected that consistency would increase with age as a result of fami liarization and fidelity to foraging sites. Immature neritic green turtles were sampled at a single foraging ground, whereas adult green turtles were sampled from a nesting population composed of individuals from multiple foraging aggregations. Therefore I expected to find differences in the degree of individual specialization as a consequence of variation in the total niche width, as the isotopic niche varies with geographic location of the foraging ground ( Chapter 3 ). I predicted that neritic juvenile s from a single foraging ground would compose a specialist population, whereas I expected adult turtles from a nesting population to compose a generalist population of specialist individuals. Materials and Methods Sample Collection Scute samples were col lected from 43 green turtles in two locations (Table 5 1 ). S amples were collected from the posterior medial region of the second lateral scute (see Rei ch et al. 2007) using a 6 mm Miltex biopsy punch after cleaning the region with isopropyl alcohol swabs. Scute samples were collected from 40 juvenile green turtles in Union Creek, Great Inagua, Bahamas, in October and November 2009. Straight carapace length (SCL) was measured with calipers from the anterior midpoint of the nuchal scute to the tip of the

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101 longer posterior marginal scutes (Bolt en 1999) To standardize measurements to the curved carapace length (CCL) used for adults, SCL measurements were converted to CCL using a regression deve loped with 1421 juvenile green turtles from Union Creek encompassing the size range of the sample population (CCL = 1.04*SCL 0.35, R 2 = 0.997) (Bjorndal and Bolten unpubl. data). Fourteen of these turtles had previously been captured and were identified by flipper tags, whereas 26 were considered recent recruits because they lacked tags and were small (CCL < 47.0 cm). Not all recent recruit samples were used in the analyses (see Results). Samples from Tortuguero, Costa Rica, were collected from 21 adu lt females in July 2009 Individual turtles had been killed by jaguars approximately 1 30 days prior to sample collection. Minimum curved carapace length (CCL) was measured from the anterior midpoint of the nuchal scute to the posterior notch at the midl ine (Bolten 1999) CCL measurements could not be made on four of the 21 turtles because they were positioned ventral side up. Samples from Tortuguero were air dried and samples from Inagua were stored in 70% ethanol prior to preparation. Twelve tissue types from different species, including green turtle skin, showed no effect on isotopic composition of the tissue from preservation in 70% et hanol (Barrow et al. 2008) Sample Preparation and Analysis Scute samples were rinsed with deionized water and dried at 60C for 24 hours. Scutes from juvenile turtles w ere lipid extracted using an ASE300 accelerated solvent extractor (Dionex) and petroleum ether solvent. The C:N ratio of loggerhead scute is 3.26 (Vander Zanden unpubl data) which less than the 3.5 ratio suggested for lipid removal or mathematical corre ction (Post et al. 2007) I assumed green turtle scute is

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102 simi lar to loggerhead scute, and therefore, that lipid extraction would not signific antly alter the isotopic value of scute samples. Each scute biopsy was glued to a glass slide, and successive 50 m layers were obtained using a carbide end mill. This interva l was selec ted as the smallest interval that could provide sufficient sample for stable isotope analysis. As scute grows outward, the oldest portion is on the exterior portion of the sample until it is sloughed off; the youngest layer is the interior, lo west section of the scute. Carbon and nitrogen isotope composition were measured at the Department of Geological Sciences, University of Florida, Gainesville, FL, using an ECS 4010 elemental analyzer (Costech) interfaced via a ConFlo III to a DeltaPlus X L isotope ratio mass spectrometer (ThermoFisher Scientific). Delta notation is used to express stable relative to the standard (5 1) where R sample and R stand ard are the corresponding ratios of heavy to light isotopes ( 13 C/ 12 C and 15 N/ 14 N) in the sample and international standard, respectively. Vienna Pee Dee Belemnite was used as the standard for 13 C and atmospheric N 2 for 15 N. The reference material USGS40 (L glutamic acid) was used to normalize all results The standard deviation of the reference material was 0.20 13 C (n = 53) and 0.15 15 N values ( n = 50). Repeated measurements of a laboratory reference material, loggerhead scute were used to examine consistency in a homogeneous sample with similar isotopic composition to the epidermis samples. The standard deviation of the loggerhead scute was 13 15 N values ( n = 21).

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103 Scute Growth Rate Scute growth rate was e stimated by methods similar to those used by Vander Zanden et al. (2010) Each 50 m section was estimated to represent a period of approximately 72 days (0.20 yr) in juveniles and 148 days (0.41 yr) in adults, using the scute record and growth rate of a resident juvenile in this study that contained an ontogenetic shift and then scaling the time period estimated for juvenil es to an adult body mass. The juvenile turtle was originally captured in Union Creek, Inagua, in July 2008 at a size of 39 cm SCL and recaptured in November 2009 at a size of 47.4 cm, resulting in a mean growth rate of 6.3 cm yr 1 over that time period. If the turtle was assumed to ha ve recruited at a size of 30 cm, using the size of the smallest turtles seen in the site and grew at the same rate prior to its capture, it would have been in a neritic zone for ap proximately 2.8 years. The 700 m scute sam ple from this turtle captures a complet e oceanic to neritic shift (Figure 5 2 A and B ), and assuming all the sampled scute tissue was deposited since the shift to the neritic zone, each layer represent s approximately 72 days. The adult scute turnover time is slower than that in juveniles, as a result of a difference in body mass, and isotopic turnover rates have been shown to scale with body mass to the 0.25 power (Carleton & Martnez del Rio 2005, Bauchinger & McWilliams 2009) At the midpoint of the size range in the neritic zone (SCL =38.7 cm), the body mass of the juvenile turtle used in this example would have been approximately 7.2 kg using a previously published conversion (Bjorndal & Bolten 1988) The average body mass of an adult female nesting at Tortuguero is 128 k g (Bjorn dal & Carr 1989) Therefore, by scaling the scute turnover time in juveniles to the appropriate

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104 adult body size, the estimated time period represented in each 50 m layer of adult scute is nearly twice that of the juveniles, or 148 days. Data Analysis The degree of individual specializatio n is often measured with diet data in a quantitative framework that incorporates the dietary variation (Roughgarden 1972, Bolnick et al. 2002) One metric of individual specialization uses the calculated ratio of the within individual component of variation (WIC ) to the variation of the population, or the total niche width (TNW). The variance between individuals (BIC) plus the WIC is equal to TNW. WIC/TNW values close to 0 indicate specialist individual s, and values close to 1 indicate generalist individuals (Bolnick et al. 2002) Because most metrics of individual specialization rely on dietary information, Newsome et al. (2007) suggested converting variance in space to p space, that is, to use using mixing models to convert isotopic values ( space) to dietary proportions (p space). Without samples of potential diet items, I am unable to convert the isotope data into dietary proportions. I simply use the tissue isotope values as a proxy for the ecological niche occupied by the individual (Bearhop et al. 2004) By using the variance in 13 C and 15 N values, the ANOVA framework provides a method to compare variation between and within individuals (Matich et al. 2011) The mean sum of squares within individuals ( MSW ) measures the variability within individuals and serves as a proxy for WIC, (5 2) The mean sum of squares between individuals ( MSB ) measures the variability between individuals and is a proxy for BIC.

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105 (5 3) where i represents an individual, j represents a single scute layer, N is the total number of obse rvations, and k is the number of individuals. I use the mean variability within individuals, or the WIC approximated by MSW, as a measure of temporal consistency to address the first objective of the study. The sum of MSB + MSW represents TNW, and I use these measures to calculate WIC/TNW as a metric of individual specialization to address the second objective of the study. Wilcoxon signed rank tests were used to compare turtle size and number of layers between the two juvenile groups. Variance in WIC and WIC/TNW calculations and comparisons of statistical significance were calculated through non parametric bootstrapping with 1000 replications. All statistics were performe d using R (R Development Core Team 2011) Results Scute Records Oceanic juveniles Twenty six j uveniles caught in Union Creek during the sampling period did not have flipper tags and were therefore assumed to be recent recruits. Stable i sotope patterns in their scutes (Fig ure 5 3 ) were used to identify individuals that still contained a record of t he oceanic phase. The oceanic stage is characterized by low 13 C values and high 15 N values, whereas the neritic stage is characterized by high 13 C values and low 15 N values (Reich et al. 2007) The scute records of four individuals had only stable isotope values reflecting only the oceanic habitat and contained no evidence of an ontogenetic shift to the neritic phase (Fig ure 5 3 A and B ). These were also the smallest of th e 26 turtles with CCL measurements < 33

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106 cm, and had likely just recruited to the coastal area. Fourteen juveniles had scute records containing the complete history of both oceanic and neritic foraging life stages with the isotope values reflec ting the on togenetic shift (Figure 5 3E and F ). Of the 14 turtles, four individuals had four or more layers representative of the oceanic life stage Therefore, eight juvenile turtles contained sufficient records to assess temporal consistency and degree of individ ual specialization in the oceanic life stage (Table 5 1, Fig ure 5 4A and B ). The time period represented in these records encompasses 0.8 to 2.0 years. N eritic juveniles Fourteen juveniles that had previously been captured and tagged in Union Creek wer e considered residents (Table 5 1). These turtles were significantly larger than the eight juveniles with oceanic layers (mean CCL: 51.0 vs. 37.3 cm; p < 0.001) and significantly more 50 m layers were obtained from the whole scute (mean layers: 9.1 vs. 5.3; p = 0.007), representing time spans of 1.4 to 2.8 yr. Most of the resident turtles had high 13 C values and low 15 N values (Figure 5 5A and B ). The majority of the turtles in this g roup had been in the neritic foraging ground for sufficient time to have lost the record of their oceanic stage. A single turtle that demonstrated a complete oceanic to neritic shift was excluded from calculations of temporal consistency and degree of ind ividual specialization, as not all layers represented habitat and diet in the neritic life stage. Adults The estimated time period represented in each scute record of adults was longer than that of juveniles, ranging from 2.4 to 6.5 yr (Fig ure 5 6A and B ). There was no significant difference between the number of 50 m layers obtained from adult scute samples and neritic juvenile samples ( mean layers: 10.3 vs. 9.1 respectively ; p = 0.1).

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107 Temporal C onsistency and Individual Specialization Serving as a m etric for temporal consistency, the mean within individual variance (WIC) for 13 C values decrease d with increasing age, and adults were significantly more consist ent than oceanic juveniles (Figure 5 7A Table 5 2). M ean within individual variance in 15 N values was not significantly different between oceanic and neritic juveniles but was significantly lower in adults than in either juvenile life stage (Fig ure 5 7A Table 5 2). The degree of individual specialization (WIC/TNW) was approximated through th e ANOVA framework. All life stages had WIC/TNW values < 0.15, indicating individual speci alization occurs across ontogenetic life stages The degree of individual specialization was similar between the two juvenile life stages (Fig ure 5 7B ), but neritic and oceanic juveniles had WIC/TNW ratios that were significantly higher than adults for both 13 C and 15 N values, indicating they are less individually specialized. Adults were the most individually specialized life stage as they had smaller WIC values and larger TNW values (Table 5 2). Discussion A general concern for examining trophic variability within a population includes the spatial and temporal scales at which individuals are sampled (Layman et al. 2012) Measures of resource use variation such as WIC/TNW often do not include the time scale over which the niche variation was observed, and many studies are based on one time samples (Bolnick et al. 2003) In this study, I added the time dimension. I n ot only have a r ecord s of resource use history from the same individuals that enable me to qua ntify temporal consistency over time but I can also examine these records relative

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108 to the whole population and calculate the time period over which the measure of individual specialization occurred. Examination of feeding patterns and resource use can he lp reveal ecological interactions and community structure (Layman et al. 2007b, Nagelkerken et al. 2006) interactions between available resources and maxim ization of potential benefits such as net energy intake or reproductive success; thus possible tradeoffs can constrain resource use (Bolnick et al. 2003) Both diet and habitat specialization in adult birds have been shown to affect reproductive success through differences in clutch size, hatching rate and fledgi ng success (Annett & Pierotti 1999, Golet et al. 2000, Hoye et al. 2012) Among other species, foraging specialization has been observed to fluctuate according to species specific ontogeny. F or instance, f oraging behavior of the scale eater fish ( Plecodus straeleni ) is more individually specialized in adults than in subadults (Nshombo 1994) In contrast, bird winged grasshoppers ( Schistocerca emarginata ) are specialist feeders as juveniles and become more generalist as adults (Sword & Dopman 1999) In the case of damselfish, Dascyllus aruanus the effect o f ontogeny on the degree of individual specialization is negligible, compared to the influence of group density (Frdrich et al. 2 010) In this study, I found t hat temporal consistency and individual specialization in re source use of green turtles varied among life stages. Comparison Among Green Turtle Life Stages I ntegrated diet and habitat use was more constant than predicted in oceanic juveniles, which displayed similar temporal consistency to neritic juveniles, despite the likely opportunistic feeding strategy in the oceanic environment (Bolten 2003) Even if

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109 oceanic juveniles feed opportunistically, they may encounter a consistent mixture of prey within the same trophic level or they may feed on prey of different trophic levels with a consistent mean isotope value Frequent prey items in the gastro intestinal contents of oceanic green turtles in the North Pacific include pyrosomas, salps, ctenophores, and cnidarians (Parker & Balazs 2005) Because stable isotope values are also influenced by habitat, if oceanic turtles re main in a foraging region with consistent isotopic values at the base of the fo od web this could also contribute to the temporal consistency observed in the oceanic life stage. L ow WIC/TNW values observed for all life stages indicate that individual specialization occurs throughout ontogeny, so that all populations most resemble g eneralist type B populations. T he degree of specialization however, changes with life stage. Oceanic juveniles are more generalist individuals than adults as expected. The neritic juveniles in this study had been resident on the foraging ground for at least a year. Of the 14 resident juveniles, only one exhibited evidence of a complete shift from the oceanic habitat, and three others contained trailing 13 C values suggestive of the shift. Using a previous characterization of oceanic and neritic isotop ic patterns (Reich et al. 2007) a 13 C value of approximately from neritic habitat use with the latter group displaying values > After removing t he turtle with the complete shift, there were no other 13 C values < I am confident that layers with 13 C values > were not deposited in the oceanic habitat, though some may represent the isotopic transition to the neritic habitat. These trailing 13 C values as well as oscillations in 15 N values contributed to the higher mean individual variance in this life stage in comparison to adults. Neritic juveniles also

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110 had significantly higher WIC/TNW values than adults, indicating a lower degree of individual specialization. Contrary to my prediction the WIC and WIC/TNW patterns of neritic juveniles were more similar to oceanic juveniles and less similar to adults. Th e observed variation in isotope values may be a co nsequence o f diet differences that arise as the juveniles adapt to a new environment and feeding strategy, with possible ingestion and assimilation of animal matter, which in particular can particularly influence 15 N values. Sponge consumption is highest in the smallest green turtles (8 kg) found in Great Inagua, Bahamas, and this size class also digests a significantly smaller portion of the nutrients in the seagrass T. testudinum than larger turtles (Bjorndal 1979) It may take at least two months to acquire the gut flora to adequately digest a seagrass diet (Bjorndal 1997b) and thus the shift to a herbivorous diet may not be abrupt for all individuals. Growing juveniles may also selectively ingest items with the highest digesti bility and protein content to maximize growth (Bjorndal 1980, Gilbert 2005) The time required for transition to an herbivorous diet and degree of dependency on other food items may be site dependent. Wherea s juvenile green turtles in a Florida lagoon do not consume any animal matter (Mendona 1983) juvenile green turtles in n eritic areas of the eastern Atlantic maintain more generalist or omnivorous foraging patterns, and do not appear to become exclusive herbivores (Cardo na et al. 2009) O nce Caribbean turtles transition to an herbivorous diet, however, they can exploit a constant food source of palatable seagrass ( T. testudinum ) with low predation threat and minimal competition The tradeoff for adopting this foraging strategy may include slower growth rate, delayed sexual maturity, and reduced reproductive output (Bjorndal 1985)

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111 Individual adu lt green turtles were highly consistent in resource use through time with the lowest mean individual variation among the three life stages. Adult scute samples contained many layers, and with each layer representing more time than in juveniles, the observ ed temporal consistency in adults spanned the longest time period. Adults also had significantly lower WIC/TNW values than both juvenile life stages, indicating a higher degree of individual specialization. Adult green turtles ex hibited a wide range in i sotope values at the population level (Table 5 2), revealing highly specialized individuals in a generalist population, similar to the pattern observed in adult loggerheads (Vander Zanden et al. 2010) Long term individual specialization in diet was suggested for large juvenile and adult green turtles in Australia, using stable isotope analysis of skin, which likely repres ents a period of several months, complemented by stomach lavage and video observations (Burkholder et al. 2011) This study indicates that consistency in green turtle diet and habitat use may extend over a period of several years. Isotopic variation is not necessarily synonymous with dietary variation (Matthews & Mazumder 2004) as individuals green turtles with the same diet could vary because of spatial differences at the base of the food web. The nesting population of green turtles at Tortuguero is composed of individuals that m igrate from multiple foraging grounds across the Caribbean (Trong et al. 2005) Previous research indicated that much of the isotopic variation among individuals in the Tortuguero nesting population is a consequence of geographic variation in the isotope values of the primary diet item ( T. testudinum ) across the Greater Caribbean ( Chapter 3 ). Therefore, I conclude that general ization in resource use observed at the population level is principally a

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112 consequence of differenc es in foraging location, but that specializa tion among individuals is caused by temporal consistency in diet at a given foraging area over a period of 2 6 yea rs. On the other hand, population level generalization documented in Australian green turtles is caused primarily by differences in diet among individuals (Burkholder et al. 2011) The results from this study indicate that ind ividuals have high fidelity to foraging regions, despite regular migrations of up to hundreds of kilometers to nesting areas. Other studies of adult green turtles have also demonstrated a high degree of fidelity to foraging regions following nesting bout s (Limpus et al. 1992, Broderick et al. 2007) The degree of individ ual specialization (WIC/TNW) can be affected by differences in WIC, TNW, or both. These comparisons among life stages highlight the effect of TNW on the degree of individual specialization. Neritic juveniles from a single foraging ground have lower TNW t han do adult turtles originating from multiple foraging grounds, or oceanic turtles that are likely using many foraging areas (M. Lpez Castro unpubl. data) (Table 5 2). The TNW also varies between oceanic and neritic juveniles but both life stages have similar WIC/TNW values because of corresponding differences in WIC (Table 5 2) Therefore, I caution that the index of individual specialization may not be comparable among populations with distinct TNW values. Scute Growth Rates I estimated the time peri ods reflected in juvenile and adult scute, but more information is needed about sea turtle scute growth and wear rates. Juvenile growth is rapid (Bjorndal et al. 2000) and thus scute tissue of the carapace also is likely to grow quickly and undergo rapid replacement in this life stage, as indicated by the shorter time period represented in a single juvenile scute layer compared to that of adults. Rapid

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113 juvenile scute growth is also supported by the short time duration (38 172 day s) that satellite transmitters remain attached to juvenile sea turtles (Mansfield et al. in press) compared to longer time periods (67 423 days) in adults (Trong et al. 2005, Blumenthal et al. 2006) Scute growth rate also appears to be species dependent, as t he estimated time period represented in a 50 m scute layer from adult green turtles is shorter than that of adult loggerheads (Vander Zanden et al 2010) T hus the maximum time reflected in a whol e scute sample from green turtle s is shorter than that for loggerheads E xternal scute layers in marine turtles are lost by mechanical wear and sloughing, and these layers are composed of the oldest tiss ue (Day et al. 2005) The loss of scute tissue is somewhat irregular, and the precise mechanism of loss is unknown, but the process al lows for an increase in surface area with age while maintaining a relatively constant thickness over time (Alibardi 2005, 2006) Outcomes Little was known about green turtles in the oceanic stage, and results of this study indicate their foraging patte rns are more consistent than previously thought Adult green turtles in this study were found to maintain the most consistent stable isotope values over the longest time span, with greater individual specialization than in juvenile life stages. This is i ndicative of high fidelity to a foraging location and dietary consistency within the foraging site. The ecological role of green turtles in the Caribbean is that of a major seagrass grazer (Bjorndal & Jackson 2003) Neritic juveniles display more variation in their scute records than adults, suggesting that their ecological role may be less con stant after they have recruited from the oceanic phase. The degree of specialization appears to be life stage dependent in green turtles, but is

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114 highly influenced by the total niche width of the population. More information is needed about whether indivi dual differences among foraging site s ( Chapter 3 ) or diet (Hatase et al. 2006, Burkholder et al. 2011) have consequences for fitness or if n utritional history in earlier life stages affect s long term survival and growth (Roark et al. 2009) A s major consumers within these marine ecosystems, it is important to recognize that temporal consistency and degree of individual specializat ion in green turtles can vary with life stage and that not all individuals are ecologically equivalent.

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115 Table 5 1 Scute samples were collected from three life stages of green turtles at two sampling locations. Sampling l ocation Life stage Year Number of individuals Range of layers CCL min max, mean (cm) 13 C min max, 15 N min max, Inagua, Bahamas Oceanic juveniles 2009 8 4 10 32.6 46.7 37.3 18.8 12.7 6.1 4.5 8.7 4.2 Inagua, Bahamas Neritic juveniles 2009 14 7 14 46.0 6 2.3 51.0 11.2 5.5* 5.7 2.3 4.0* 6.3 Tortuguero, Costa Rica Adults 2009 21 6 16 99.0 112.5 105.5 13.0 6.5 6.5 2.5 9.9 7.4 For each life stage the year of collection, number of individuals, range of the total number of scute layers, mean siz e plus range (curved carapace length), and range in carbon and nitrogen stable isotope values are indicated. *One individual was removed from the calculation of the isotopic ranges of the neritic juveniles due to several layers that r epresented the transit ion betwee n oceanic and neritic habitats

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116 Table 5 2. Within individual contribution (WIC) and total niche width (TNW) approximated through the ANOVA framework among three life stages. Life stage 13 C WIC 13 C TNW 15 N WIC 15 N TNW Oceanic juveniles 0.9 5 10.68 0.34 6.09 Neritic juveniles 0.60 4.89 0.72 10.92 Adults 0.32 13.57 0.17 28.54 Carbon and nitrogen isotope values were compared separately.

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117 Fig ure 5 1 Conceptual model of resource use and predicted patterns in nitrogen stable isotope valu es A) S pecialist p opulation. B) G eneralist population type A comp osed of generalist individuals C) Generalist population type B composed of specialist individuals Arrows track individuals, and e ach circle represents the 15 N value for a layer of ine rt tissue which reflects resource use. Modified from Vander Zanden et al. (2010)

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118 Figure 5 2. S table isotope values in successive 50 m subsections of scute in a single neritic juvenile green turtle from Inagua, Bahamas. A) Carbon isotope values. B) Nitrogen isotope values. The scute sample from this turtle captures a complete oceanic to neritic shift and was used to determine the tim e period represented in each 50 m scute subsection. A B

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119 Figure 5 3 S table isotope values in successive 50 m subsections of scute in 26 juvenile green turtles from Inagua, Bahamas, that were untagged when sampled. The individuals were divided by the patterns in their isotopic records and classified into four groups. A) Carbon isotope values of recent recruits to the habitat B) Nitrogen isotope values of recent recruits ( n = 4). C) Carbon isotope values of residents D) Nitrogen iso tope values of residents (n = 3) E) Carbon isotope values of recruits that retain a history of the ontogenetic shift from the oceanic habitat F) Nitrogen isotope values of recruits that retain a history of the ontogenetic shift from the oceanic habitat (n = 14) G) Carbon isotope values of recruits that show an incomplete ontogenetic shift H) Nitrogen isotope values of recruits that show an incomplete ontogenetic shift (n = 5). Increasing distance from the lower surface of the scute sample correspon foraging history. Individuals are represented by unique symbols. A B C D E F

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120 Figure 5 3 C ontinued G H

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121 Figure 5 4 S table isotope values in successive 50 m subsections of scute in 8 juvenile green turtles from Inagua, Bahamas, representing the oceanic life stage. A) Carbon isotope values. B) N itrogen isotope values. Increasing distance from the lower surface of the scute sample corresponds to older time symbols. Some individual records do not begin at 50 m, as only the layers that reflect the oceanic life stage are depicted A B

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122 Fig ure 5 5 S table isotope values in su ccessive 50 m subsections of scute in 14 neritic juvenile green turtles from Inagua, Bahamas, that were considered to be residents because flipper tags had been applied one year prior to sampling. A) Carbon isotope values. B) Nitrogen isotope values. I ncreasing distance from the lower surface of the scute sample corresponds to older time periods symbols. The individual with the longest record, represented with a crossed diamond, w as used to estimate the time period represented in each scute layer and was removed from statistical analysis. A B

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123 Figure 5 6. S table isotope values in successive 50 m subsections of scute in 21 adult green turtles from Tortuguero, Costa Rica. A) Carbon isotope values. B) Nitrogen isotope values. Increasing distance from the lower surface of the g history. Individuals are represented by unique symbols. A B

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124 Figure 5 7. Metrics to compare temporal consistency and individual specialization among life stages: oceanic juveniles, neritic juveniles, and adult green turtles. A) Me an var iation within individuals or WIC, as calculated with MSW from the ANOVA framework B) T he degr ee of individual specialization or WIC/TNW, as calculated with MSW/(MSW+MSB). WIC/TNW ratio can range from near 0 when all individuals are specialists to 1 wh en all individuals are generalists. All points represent mean 1SD. Pairwise comparisons were conducted A B c c d c c d a a b a a b b

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125 separately for 13 C and 15 N values, and pairs that do not share letters are significantly different. See the text for acronym definitions.

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126 CHAPTER 6 CONCLUSIONS AND FURTHER RESEARCH Fundamentals Sea turtles interact with their environment at a fundamental level through their foraging. My research seeks to understand the foraging ecology of loggerheads and green turtles with the tool of stable isoto pe analysis. In 1983 this methodology was first applied to loggerhead sea turtles foraging along the US coast (Killingley & Lutcavage 1983) T he stable isotope ratios in the epibiotic barnacles found on the shells of loggerheads were used to distinguish turtles using estuarine and coastal waters Since then, and notably over the past decade, th is technique has been increasing dramatically in the field of sea turtle biology Stable isotope ratios of carbon and nitrogen in consumer tissu e reflect both diet and habitat One major appli cation of stable isotope analysis has been t he assess ment of the proportion of potential diet items through m ixing models (Phillips & Gregg 2001, Moore & Semmens 2008, Parnell et al. 2010) This method has been applied to wild populations of loggerheads and green turtles to b etter understand foraging patterns (Wallace et al. 2009, McClellan et al. 2010, Lemons et al. 2011) However, these mixing models rely on the use of appropriate discrimination factors, or the isotopic offset which are fundamental to the accuracy of the models Early studies of discrimination factors reported fairly consistent offsets for carbon and nitrogen isotope values (DeNiro & Epstein 1978, Minagawa & Wada 1984) and these were confirmed by la ter reviews (Vander Zanden & Rasmussen 2001, Post 2002) Further research has revealed that discrimination factors may be more complex and

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127 variable than originally thought, with potential influence of tissue type, diet, protein quality, growth, and sp ecies (Martnez del Rio & Wolf 2005, Caut et al. 2009, Robbins et al. 2010) I investigate some of these factors in Chapter 2. I directly examine the tissue and life stage differences, and indirectly compare d iet and species differences. I have found that the discrimination factors in green turtles are dependent on tissue, growth, diet, and species. T herefore this research contributes to creating more accurate diet reconstructions in future research by provi ding appropriate discrimination factors Stable Isotopes Never Lie Jim Ehleringer, a distinguished researcher in the field of stable isotope ecology who directs a n annual course on the subject, has stated that stable isotopes never lie but s ometimes the difficulty can reside in the interpretation of stable isotope data Deciphering such data from a nesting population of green turtles in Chapter 3 provides an example of his precaution and why care is needed to translate stable isotope data into meaningfu l conclusions regarding the biology of the organism of interest. After accounting for discrimination between tissue and diet t he stable isotope values I initially measured in the nesting population of Tortuguero suggested that the green turtles could be feeding over multiple trophic levels. Whereas green turtles have been traditionally viewed as major seagrass consumers in the Greater Caribbean the multiple trophic level interpretation of the data could have been realistic, given the carnivorous and omni vorous foraging patterns that have been observed in other green turtle populations worldwide (Hatase et al. 2006, Amorocho & Reina 2007, Arthur et a l. 2007, Lemons et al. 2011) Limited stomach content data for Caribbean green turtles had not revealed any major consumption of animal matter (Mortimer 1981, Bjorndal 1990) yet it would have been possible that this

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128 method could have missed individuals using alternative foraging strategies. I had also eliminated individual variation as a major contributor to isotopic variation in the population (Chapter 2). I n the absence of further investigation, I could have erron eously concluded that carnivory is an important foraging strategy in the population. However, sampling turtles from multiple foraging grounds and compound specific stable isotope analysis of amino acids revealed that the green turtles in the Greater Caribbean are in fact herbivorous, but the stable isotope variance I observed is primarily a result of s patial varia tion at the base of the food web That is, the ocean basin is not geographically homo geneous in the stable isotope values of primary producers, and these differences are passed up the food web. Mapping and understanding broad scale geographic patterns in stable isotope values is a major branch of isotope ecology (Hobson et al. 2010, Graham et al. 2010, Jaeger et al. 2010b) The marine isoscape patterns in the Gulf of Mexico and the Caribbean have not been well defined, but identifying consistent differences among regions in these ocean basis can provide a powerful tool for understanding sea turtle biology, particularly for migration pattern s The connectivity between nesting and foraging grounds has been of great interest to sea turtle biologists. Because o f the difficulties monitoring foraging populations, much research has been focused at nesting beaches where there are fewer logistical constraints. In Chapter 3, I demonstrate that samples collected from the nesting population can provide information abou t the foraging areas used prior to the nesting season. Thus, stable isotope analysis contributes another method to the tool belt of sea turtle biologists who have used flipper tags, satellite telemetry, and genetic mixed stock

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129 analysis to measure the migr atory connectivity between nesting and foraging populations. Creatures of Habit The time period represented tissue is dependent upon the met abolic properties of the tissue, which affect the turnover time, or re placement period Some tissues are rapidly replaced such as plasma, whereas other tissues such as skin and muscle, represent longer time periods. Finally, o ther tissues such as hair, tusks, and feathers are inert after synthesis and may represent the lon gest time periods, depending on the physical retention time of the se structures Sea turtle scute is a keratin based tissue on the carapace that represents long term diet and habitat use and can be subsampled to examine consecutive time intervals Unfort unately, sc u te is not retained for the lifetime of th e animal, such as elephant tusks (Codron et al. 2012) but it may represent a period of up to 12 years in loggerhead sea turtles This tissue provides the opportunity to examine the long term history in sea turtle resource use incl uding consistency and specialization, which I investigated in both loggerheads and green turtles (Chapters 4 and 5). Bearhop et al. (2004) first suggested that the variance of stable isotope ratios in a population could be used as a measure of niche width, and Layman et al. (2007a) later develop ed metrics to describe the extent of spacing among points in the carbon and nitrogen bi plot and the relative positions of those points to quantify the stable isotope niche With single samples representing a short time period, the stable isotope variatio n in a population could be used to distinguish between specialist and generalist populations but could not distinguish between the two types of generalist populations ( Figure 6 1). Alternatively serial sampling of individuals could provide sufficient dat a to

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130 distinguish between all three population types, which is precisely what scute allows us to do but with a single sample from each individual. The conceptual model in Figure 5 1 incorporates the consistency of the isotope values in scute layers throug h time to distinguish between individual specialists and individual generalists in the two types of generalist populations. Therefore, the level of consistency through time is crucial to characterizing individual patterns of foraging within a population. Comparing the individual variation (or consistency) in isotope values to the total isotopic niche of the population can then provide a measure of individual specialization. Often, the patterns of individuals are overlooked or missed when population data are combined (Bolnick et al. 2003) The scute pat terns of loggerhead sea turtles revealed long term consistency in resource use through time, with isotopi c differences among individuals, which prompted me to conclude that the generalist population was composed of specialist individuals. However, I was u nable to distinguish between diet and habitat as the cause for the wide range in isotope values and generalization at the population level at the time Chapter 4 was published and could only conclude that consistently used distinct subsets of the available resources I indicated that the differences were likely not a result of diet, but rather to geographical variation at the base of the food web. Additional research has since confirmed my suspicions. A combination of stable isotope analysis and satellit e telemetry revealed that variation in the 13 C and 15 N values of male loggerheads in the Northwest Atlantic is significantly correlated to latitude, and there are clear geographic patterns at the base of the food web that differ because of the predominan t biogeochemical processes in each region (Pajuelo et al.

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131 2012) Similar patterns have been found for female loggerheads, and the foraging location from which nesting females originated can be determined as a result of the c onsistent stable isotope ratios in each of the three geographic regions utilized by the loggerheads in the coastal Northwest Atlantic (Pajuelo et al., unpubl. data) Another study of loggerheads in Western Australia has suggested that individuals have highly generalized diets wit h strong fidelity to foraging locations, indicating they are site specialists and diet generalists (Thomson et al. 2012) The consistency I observed in the loggerheads in Chapter 4 is thus likely to result from a combina tion of both site fidelity and diet similarity through time. In adult green turtles, I observed similar patterns of long term resource use to that of adult loggerheads: individuals specialize in the resource s use d within a more generalized population (Chapter 5) A gain, the total population isotopic niche wa s influenced by the variation in stable isotope v alues among foraging sites and green turtles appear to have high fidelity to foraging site s with a consistent herbivorous diet through time. In this species I also examined how temporal consistency and individual specialization vary through ontogeny by sampling green turtles from the oceanic and neritic juvenile life stages in addition to the adults I found that these are not fixed characteristics through the life stages, and neritic juveniles are less consistent as they adapt to a new foraging strateg y after recruiting from the oceanic life stage. Additionally, the degree of specialization is highly influenced by the total niche width of the population. Individuals from a single foraging ground in the neritic juvenile population do not share the same niche width as the adult nesting population that

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132 originates f rom multiple foraging grounds, which necessitate s caution when defining the population niche width to determine the degree of individual specialization. Although sea turtles in the same life s tage may utilize many foraging grounds, r ecognizing that individuals sea turtles are creatures of habit that is, they have high fidelity to a foraging area is important to sea turtle conservation. Stable isotope analysis can provide a method by which to i dentify the foraging areas that are used and determine how long individuals stay Onwards and Upwards Together, these studies provide examples of how our understanding of sea turtle biology and ecological roles can be enhanced through research that incorp orates stable isotope analysis I would now like to highlight some additional applications and potential directions for future research in this field Supplementary work has been conducted to examine the role of green turtles as nutrient transporters bet ween spatially separated marine and terrestrial ecosystems (Vander Zanden et al. 2012) The nutrients from green turtle nest remains (e.g. shells, chorioallantoic fluid, unhatched eggs, and hatchlings that do not emerge) at Tortuguero Beach Costa Ri ca appear to subsidize the terrestrial vegetation. Total percent nitrogen and 15 N values of beach vegetation were correlated with the nest density, indicating nutrients are derived from a marine source where more turtles nest Additionally, the dominant plant species changed between high and low nest density sites, implying that turtle derived nutrients may alter the plant community composition. There are possible ramifications for plant quality and production as well as entire community dynamics with annual nesting cycles that bring pulses of sea turtle egg

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133 derived nutrients that may be available to predators, scavengers, and detritivores, which would be an exciting topic for future research. Corollary effects such as these have been observed in other examples of biotic nutrient transfer at salmon spawning sites (Bilby et al. 2003, Bartz & Naiman 2005) and seabird rookeries (Polis & Hurd 1996, Anderson & Polis 1999, 2004) Whereas ecological roles of sea turtles have been considered mostly in the marine ecosystem, there are fewer examples of sea turtle s fulfilling ecological roles in the terrestrial environment (Bjorndal & Jackson 2003, but see Bouchard & Bjorndal 2000) Reduced population sizes have resulted in declines in sea turtles fulfilling their roles in marine ecosystems and terrestrial beach ecosystem s may also be affected by these population losses (McClenachan et al. 2006 ) Other directions for future research in applying stable isotope analysis to sea turtle ecology include determining the isotopic patterns in sea turtle eggs and hatchlings. Besides measuring the discrimination factors between sea turtle tissues and th e diet, it is also helpful to know the isotopic offset between a female sea turtle and her offspring. Because t here is a limited window of time in which to collect tissue samples from nesting females, if the female is not encountered at the nesting beach, the collection opportunity is lost. However, if hatchlings and egg components (e.g. yolk and albumen) are consistently related to the isotopic composition of female tissues sampling from the offspring or eggs could provide an opportunity to gain inform ation about the female foraging patterns when she is missed, thus increasing sample sizes. Work by an undergraduate collaborator has found the stable isotope ratios in loggerhead hatchling epidermis are significantly correlated to th ose in the females

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134 (Fran kel et al. 2012) C alculating these discrimination factors in egg components and in other sea turtle species would also be useful I have verified inherent variation is minimal in contributing to stable isotope ratios of sea turtles in wild populations but there may also be differences in the sample collection, preservation, and preparation methods that can lead to small differences which affect the interpretation of the data. The study by Frankel et al. (2012) also revealed that decomposition can affect the reli ability of stable isotope values. Other studies have examined the effect of preservation methods on sea turtle tissues (Barrow et al. 2008, L emons et al. 2012) Controlled studies are needed to determine whether factors such as sampling location for skin and scute or keratinized areas of skin affect stable isotope ratios in order to optimize collection procedures and reduce systematic variati on. T hese data can contribute to developing standardized protocols that facilitate comparison of data among studies on a global scale We are also in need of controlled studies to measure sea turtle scute growth rates. Though I provide estimates of logge rhead and green turtle scute growth rates in Chapters 4 and 5, these calculations are accompanied by several assumptions. Despite the current limitations to discern precise time periods represented in the sample, scute is an advantageous tissue to work wi th. It provides long term consecutive time intervals of resource use, a nd it can be collected non invasively from live individuals (Bjorndal et al. 2010) or from stranded animals and carcasses without concern of decomposition. Because of its inert composition, scute can be stored easily without preservation methods such as ethanol or freezing, unlike skin and blood

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135 samples. Thus, precise measurements of s cute growth rates would allow for more accurate reconstructions of sea turtle foraging histories. Finally, we have seen that nesting populations use multiple foraging grounds that differ not only in stable isotope ratios at the base of the food web, but li kely differ in characteristics such as nutrient regimes and water temperature that can affect food availability and abundance. The heterogeneity in resource use may translate to variation in reproductive output, measured by characteristics such as clutch size, number of clutches laid per season, and inter annual nesting frequency. Mediterranean loggerheads that use distinct foraging areas differ in body size and clutch size, indicating there may be fitness consequences to alternative migratory strategies (Zbinden et al. 2011) L oggerheads using different foraging areas in the Northwest Atlantic however, did not differ in fecundity measures such as clutch frequency, clutch size, body size, remigration, and inter nesting intervals (Hawkes et al. 2007) These relationships should be investigated in other species and populations. Stable isotope analysis is sometimes best combined with other analytical tools such as satellite t racking (Zbinden et al. 2011, Paj uelo et al. 2012) compound specific stable isotope analysis (Seminoff et al. 2012, Chapter 3) stomac h content analysis (N. Williams unpubl. data), or foraging observations (Burkholder et al. 2011, Thomson et al. 2012 ) There are limits to the scope of stable i sotope data to distinguish fine scale habitat and diet ary information, yet the broad scale patterns are tremendously useful. Most sea turtle studies incorporating stable isotope analysi s have utilized carbon a nd nitrogen isotopes but in some cases, additional elements with stable isotopes (e.g lead, oxygen, sulfur) or trace elements (e. g. aluminum copper, iron indium, va na dium )

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136 may be useful for resolving differences that are otherwise indistinguishable ( M. Lpez Castro, unpubl. data). Anthropogenic threats like the Deepwater Horizon oil spill have highlighted gaps in our knowledge about sea turtles, particularly in the Gulf of Mexico, where little is known regarding population trends and demography or t he major foraging areas used offshore making it difficult to assess the ecological consequences of such disasters (Bjorndal et al. 2011) Stable isotope analysis may be useful in understanding habitat use and movement patterns in t hese understudied populations. In summary, I aim to better understand sea turtle foraging and the ways in which the tool of stable isotope analysis can aid in this purpose. Identifying the role of sea turtles in maintaining the structure and function of m arine and adjacent terrestrial ecosystems can help to provide more meaningful goals for their conservation.

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137 Figure 6 1. Consumers in each population have distinct diets, with three possible prey types that vary in their stable isotope ratios A spe cialist population feed s on the same prey type and all individuals have similar stable isotope values Individuals in the generalist population type A eat all prey types whereas individuals in generalist population type B specialize on different prey ty pes. W ith single point sampling of a tissue th at represents a short time period both generalist populations would have the same mean and variance in stable isotope values, making i t is impossible to distinguish between the two Adapted from Bearhop et a l. (2004)

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138 APPENDIX GREEN TURTLE FEED INGREDIENTS Green turtles at the Cayman Farm were fed an extruded floating pellet diet manufactured by Southfresh Feeds (Alabama, USA) containing the following ingredients: Plant protein products, processed grain byproducts, grain products, animal protein products, fish meal, fish oil, dicalcium phosphate, calcium carbonate, vitamin A supplement, vitamin D supplement, vitamin E supplement, calcium panthotenate, niacin supplement, ascorbic acid (vitamin C), menadione dimethylprimidinol, disulfate, pyridoxine hydrochloride, riboflavin supplement, thiamine mononitrate, vitamin B12 supplement, folic acid, zinc sulfate, ferrous sulfate, sodium selenite, copper sulfate, manganese sulfate, ethylenediamine dihydriodide.

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154 BIOGRAPHICAL SKETCH Hannah B. Vander Zanden was born in Moscow, ID in 1981. She was interested in environmental causes and conservation in high school where she joined the Environmental Club and volunteer in a sea turtle conservati on program a t a beach near Tomatln, Mexico in 1997 and 1998. After graduating in 1999, she attended Pomona College in Claremont, CA and received a B.A. in biology with a minor in Spanish in 2003. During her summers as an undergraduate, she participated in three National Science Foundation Research Experience for Undergraduat e Programs at Pomona College, Wellesley College, and Los Alamos National Laboratory to explore research in the fields of ecology and molecular biology. These experiences inspired he r to continue in ecological research, but before attending graduate school, she joined the Peace Corps to gain more international experience. Hannah served as a Peace Corps Natural Resource Management Extension Agent from 2003 2005 in Senegal, West Africa She worked primarily with rural farmers to implement agroforestry technologies in a deforested landscape but also ssroom before beginning her PhD with Karen Bjorndal in the Department of Zoology at th e University of Florida in 2006. She worked as a research assistant for one year under the direction of Jamie Gillooly, taught labs for Introduction to Biology and Ecolo gy laboratory sections, as well as served as an online instructor for the Introduction to Biology course. During her graduate studies at the University of Florida, she was also active in the Women in Science and Engineering, Innovation through Institution al Integration Graduate Student Advisory Council, the Biology Graduate Student Association, and the Graduate Student

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155 Council. She has received a Florida Sea Turtle Grant to begin a postdoc toral position at the University of Florida in the fall of 2012.