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 FOR THE DEGREE OF DOCTOR OF PHILOSOPHY
UNIVERSITY OF FLORIDA
2012
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© 2012 Hannah B. Vander Zanden
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To Luis
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ACKNOWLEDGMENTS
Though this dissertation bears the authorship line of a single individual, there are
numerous people who have contributed 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 drafts than humanly possible. Alan Bolten has taught 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 members—Karen Bjorndal, Alan Bolten, Mark Brenner,
Patrick 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 have 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 interpretation. The members of the Archie Carr Center for
Sea Turtle Research: Peter Eliazar, Gabby Hrychyshyn, Melania López-Castro, Mariela
Pajuelo, Joe Pfaller, Kim Reich, Alison Roark, Luciano Soares e Soares, Natalie
Williams, and Patricia Zárate have contributed to providing a supportive team and
“academic family”. I thank 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, Jake 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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including: Joslyn Armstrong, Sophia Caccitore, Alice Chow, Nicole Frankel, Temma
Kaufman. My officemates 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 logistics: 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 sites 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 Turtle Farm, Walter Mustin
provided all of the samples from green turtles. In the Bahamas, Randolph Burrows,
Steve Connett, Barbara Crouchley, Henry Nixon, and the Bahamas National Trust
assisted with turtle sampling. Cathi Campbell and Cynthia Lagueux were critical in
sampling green turtles from Nicaragua 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. Nick Osman and John 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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individuals also provided helpful reviews for drafts of chapters in this dissertation,
including Steffen Oppel, Carlos Martínez 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
Park Service Permit #CANA-2004-SCI-0003. Samples 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
Energía (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 Foundation
Graduate Research Fellowship, Sigma Xi Grant in Aid of Research, PADI Foundation
Grant, University of Florida Graduate Student Council Research Award, and the
Department of Biology John Paul Olowo Memorial Research Grant. Additional funds
were provided by the Lockhart Dissertation Fellowship 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 Conservation Fund, Florida Sea Turtle Grant Program Knight Vision
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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. Without Lee Anne Eareckson—
who organized the “Turtle Trips” to Mexico in which I participated 15 years ago—I may
have taken a completely different path, as observing a sea turtle nest for the first time
was a life changing experience. I thank my family for their love and encouragement
throughout my graduate years and for their belief in the value of education. Finally, I
thank my partner, Luis Álvarez Castro, for his understanding, rational guidance, and
unwavering support over the past five years.
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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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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 Specialization ...................................... 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 ..................................................................................... 127
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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LIST OF TABLES
Table page 1-1 Abundance and standards used for stable isotopes used in this research ......... 25
2-1 BIC values for the ten models ............................................................................. 44
2-2 Mean 13C and 15N values (‰) and variance are reported 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 Discrimination factors (Δ13C and Δ15N) 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 13C and 15N 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 15N values of Tortuguero green turtle epidermis and seagrass (Thalassia testudinum) ................................................................. 73
4-1 Minimum, maximum, and mean ranges of 15N and 13C for individual scute records ............................................................................................................... 90
4-2 ANOVAs indicate significant differences between the means of individuals ....... 91
5-1 Scute samples were collected from three life stages of green turtles at two locations ........................................................................................................... 115
5-2 Within individual contribution (WIC) and total niche width (TNW) approximated through the ANOVA framework among three life stages ........... 116
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LIST OF FIGURES
Figure page 2-1 Results of three models using the first parameterization in which mean and
variance are estimated ....................................................................................... 49
2-2 Values of 13C and 15N and bivariate 95% confidence ellipses for each tissue and life stage ............................................................................................ 51
2-3 Comparison of isotopic variation in epidermis samples 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 13C and 15N values of green turtles and seagrass ......................... 76
3-3 Difference in 15N values between each amino acid and phenylalanine among primary producers ................................................................................... 77
3-4 Green turtle trophic position................................................................................ 78
3-5 The relationship between the bulk epidermis 15N and phe 15N values ............ 79
4-1 Conceptual model representing resource use through time. .............................. 92
4-2 Values of 15N and 13C values in successive scute layers from 15 loggerheads ........................................................................................................ 93
4-3 Example of a shift in resource use. ..................................................................... 94
4-4 Biplot of 13C and 15N 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 illustrating 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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5-6 Stable isotope values in successive subsections of scute in 21 adult green turtles ................................................................................................................ 123
5-7 Temporal consistency 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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LIST OF ABBREVIATIONS
AA-CSIA Compound-specific stable isotope analysis of amino acids
ANOVA Analysis of variance
BIC Bayesian information criterion, between individual component of variation
CCL Curved carapace length
CTF Cayman Turtle Farm
DERM Dermis
GLU Glutamic acid
EPI Epidermis
JUV Juvenile
MANOVA Multivariate analysis of variance
MSB Mean sum of squares between individuals
MSW Mean sum of squares within individuals
PHE Phenylalanine
PLA Plasma
RAAN Región Autónoma del Atlántico Norte (Northern Atlantic Autonomous Region)
RAAS Región Autónoma del Atlántico Sur (Southern Atlantic Autonomous Region)
RBC Red blood cells
SCL Straight carapace length
SD Standard deviation
SRCAA Source amino acid
TEF Trophic enrichment factor
TNW Total niche width
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TP Trophic position
TRAA Trophic amino acid
WIC Within individual component of variation
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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 Philosophy
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 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
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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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CHAPTER 1 BACKGROUND OF SEA TURTLE BIOLOGY AND STABLE ISOTOPE ECOLOGY
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 turtles 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 nesting 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 approximately two months after their deposition, and
hatchlings move from nesting beaches to oceanic habitats via major ocean currents,
commencing a period termed the “lost years” by Archie Carr (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 dietary 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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grounds (Lohmann et al. 1997), and undergo regular migrations between foraging and
nesting 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
understand 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 loggerheads 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
Stable 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 number 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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applications: mainly hydrogen, carbon, nitrogen, oxygen, and sulfur. In my work, I have
used carbon and nitrogen stable isotopes, with heavy isotopic 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 field 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. The part per thousand unit in delta notation is termed “per
mil” and is denoted with the symbol ‰. A positive 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 that is needed to activate
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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 fractionation 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
precipitation 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 isotopic gradients 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 isotope 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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Applying 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 as 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 in 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 green 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
small 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 discrimination
factors for each of the four tissues to compare between life stages and among other sea
turtle species. Both life 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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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 diet 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 discrimination 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 about the base of the
food web, this required further investigation. With bulk and compound-specific stable
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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citation will be: Vander Zanden HB, Arthur KE, Bolten AB, Popp BN, Lagueux CJ,
Harrison 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 material
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 specialization
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 but 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 citation 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. Biology Letters 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 that 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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Both temporal consistency and individual specialization 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 roles 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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Table 1-1. Abundance and standards used for stable isotopes used in this research (adapted from Michener & Lajtha 2007).
Element Isotope Abundance Relative mass difference (%)
International standard
Carbon
12C
13C
98.892
1.108 8.3
Vienna Pee Dee Belemnite (VPDB)
Nitrogen
14N
15N
99.365
0.365 7.1
Atmospheric nitrogen (air)
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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 (the difference
between stable isotope values of an organism’s tissue and diet). More recent
applications using carbon and nitrogen stable isotope compositions (13C and 15N) to
examine trophic niche and specialization rely on measures of stable isotope variance
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 ecological
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 an individual through time. Low variation indicates specialization,
whereas substantial variation indicates generalization (Martínez del Rio et al. 2009a,
Bearhop et al. 2004, Newsome et al. 2009, Vander Zanden et al. 2010).
In many studies, isotopic variation is attributed to diet and habitat differences but
can also result from variation in the isotopic composition within a prey species, inherent
variation in the consumer, and measurement error (Bearhop 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 differences in physiology despite
consuming the same diet and experiencing controlled conditions. Although not often
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quantified, inherent variation could substantially affect conclusions based on stable
isotope data. Inherent variation can depend on the species, 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 that
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 represented as Δ = tissue – diet and results from
processes such as fractionation during metabolic transformations and isotopic routing
(Martínez 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 models (Post 2002, Wolf et al. 2009). Many
studies have used generalized 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 15N and 13C compared to their diets
(DeNiro & Epstein 1978, 1981, Post 2002), though discrimination 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
28
for nitrogen (Δ15N) is 3.4‰ (DeNiro & Epstein 1981, Post 2002). Values of Δ13C are
typically much smaller than Δ15N values, resulting in a reduced trophic shift in 13C
values as nutrients are transferred through the food web (DeNiro & Epstein 1978).
The first objective 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 examined
the variation in stable isotope values—a measure of inherent variation—in four tissue
types (epidermis, dermis, serum, and red blood cells) and two life stages (large
juveniles and adults). I then compared 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 an isotopically consistent diet. Furthermore, I
incorporated the measure of inherent variation into estimates of the discrimination
factors. I also compared the discrimination factors measured in this study with other
sea turtle species.
Materials and Methods
Study Conditions
Green 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 30 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
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 ingredients 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). The turtles were assumed to be at isotopic equilibrium with the diet.
Juveniles and adults were maintained 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 depth 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 Collection
During April and May 2010, tissue samples were collected from 30 adult female
green turtles and from 40 large 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 mL Draw CORVAC serum separator
tubes. Serum and red blood cells were separated by centrifugation at 2195 g and
frozen separately at -20°C 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
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 condition index was calculated as [mass / CCL3)]
* 104 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
isotopic differences in different food lots, I am 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
experienced the variation equally.
Sample Preparation and Isotope Analysis
Serum and red blood cell samples were thawed, dried at 60°C for 24 hours, and
homogenized with a mortar and pestle to a fine powder. Skin samples were rinsed in
distilled water; epidermis 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 60°C for 24 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 an
31
ECS 4010 elemental analyzer (Costech) interfaced via a ConFlo III device to a Delta
Plus XL isotope ratio mass spectrometer (ThermoFisher Scientific). The standards
used for 13C and 15N were Vienna Pee Dee Belemnite (VPDB) and atmospheric N2,
respectively. Delta notation is used to express stable isotope abundances, defined as
parts per thousand (‰) relative to the standard:
= (
- ) (2-1)
where Rsample is the ratio of heavy to light isotopes (13C/12C or 15N/14N) in the
sample and Rstandard 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 standard deviation of . 2‰ for 13C and . 4‰ for 15N (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 composition to the tissue samples in this study. The standard
deviation of the loggerhead scute was . 7‰ for 13C and .25‰ for 15N (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 Analysis
Ten multivariate normal models were fit to the carbon and nitrogen isotope data to
examine how to best group the data while considering means and variances among
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 the 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 means, the variances, or both were
allowed to differ in the resulting multivariate normal 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
2002). 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 juvenile 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 hypothesis, each tissue
type was considered separately, though adult and large juvenile values of the same
33
tissue type were grouped. Hence, four different multivariate normal sampling models
were needed to explain the data. In the fourth hypothesis, the samples were 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 was 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
number 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, pairwise 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 stage, but comparisons between life stages
were made only for the same tissue type. In these pairwise comparisons, ML estimates
and BIC values were calculated first assuming that the observed samples all came from
a single sampling multivariate normal model 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 assumed to differ. Differences
34
in BIC values (ΔBIC) were calculated as BICcombined – BICseparate. A ΔBIC value greater
than 2, 6, or 10 corresponds to positive, strong, or very strong evidence, respectively,
for favoring the separate model over the combined model (Raftery 1995). Therefore,
two groups were considered significantly different in their bivariate means or variances if
ΔBIC > 2. Negative values occurred when the BICseparate was larger than the BICcombined.
Green turtle discrimination factors were calculated as Δ = 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 13C and 15N values for the diet and each
tissue for both life stages. The mean diet-tissue discrimination values ± 1SD were
calculated by running 50,000 iterations.
The relationships between body condition index and 13C dermis values were
examined using Spearman’s rank correlation. All statistical analyses were performed
using R® (R Development Core Team 2011).
Results
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 significantly
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 13C values. Re-analysis of stable isotope ratios in those samples indicated the
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
13C or 15N values (tC = -0.36, df = 5, pC = 0.73; tN = -0.86, df = 5, pN = 0.43). For all
adult turtles, 13C values in dermis were significantly correlated to body condition
(Spearman’s rank, ρ = .63, p < . ). Condition indices are often used 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 13C values (> -2 .2‰) also had high body condition index
measures, > 1.4.
Pairwise comparisons between tissue means revealed significant differences
among all tissues (Table 2-3), which led to differences in discrimination factors among
tissues. Discrimination factors between diet and turtle tissues (Δ) in adults ranged from
.24 to 2.58‰ for carbon and 2.48 to 4.93‰ for nitrogen (Table 2-5). Discrimination
factors in large juveniles ranged from .5 to 2. 8‰ for carbon and 2.36 to 4.15‰ for
nitrogen, which were substantially larger than discrimination factors previously reported
for juvenile green turtles (Table 2-4). In comparison with the adults, juvenile Δ13C
values were larger in all tissues except dermis, and Δ15N 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 13C and 15N values of the diet were -23.05 and 2.53‰, respectively (n = 12,
Table 2-2).
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, the lowest BIC values were obtained when
the data were grouped by tissue type and life stage, indicating that eight groups were
most appropriate to divide the data (Hypothesis 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 confidence
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 could 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 as 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 hypothesis 4 with the third parameterization, indicating that the variance is more
influential in driving the group differences.
37
Discussion
Inherent Variation
Measures of inherent variation can be informative for field studies. Because the
inherent variation differed among tissues, these 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‰ in 13C and 6.6‰ in 15N values
from epidermis samples (Bjorndal and Bolten 2010). In the captive population from this
study, the epidermis ranges were .8‰ for 13C and .5‰ for 15N. Moreover, the
variance in both 13C and 15N 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 distinct 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 13C values for adults
in this study, but lipids do not appear to be responsible for the observed range. The
38
measured C:N ratio of dermis in this study (2.8) falls below the cutoff of 3.5 for aquatic
animals, indicating that lipid content is likely below 5% and would not influence the 13C
values (Post et al. 2007). Also, removal of lipids did not result in significant differences
for 13C values. A relationship was observed between the dermis 13C values and the
condition index. If the condition index is an indicator of health or if it changes with
reproductive status, these factors may influence the range in adult dermis 13C values.
Based on these results, I would discourage the use of dermis as a sampling tissue.
In this study, large juveniles generally 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). The isotopic
variation previously reported in juvenile green turtle tissues (Seminoff et al. 2006) is
similar to my study (Table 2-2). Additionally, 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 were
lower in all tissues (Table 2-2).
Discrimination Factors
Mean discrimination factors of 0-1‰ for 13C and 3.4‰ for 15N were reported by
early studies (DeNiro & Epstein 1978, Minagawa & Wada 1984) and confirmed by
recent reviews (Vander 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 to be applied to isotopic mixing models for dietary
reconstructions or trophic level estimations (Caut et al. 2009). Small changes in
39
discrimination factors can lead to substantial differences in the output of these mixing
models (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
sampling 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 13C and 15N were distinct among tissue
types and life stages. These differences in tissue means translate into discrimination
factor differences. The inherent variation observed in each tissue was also incorporated
into the standard deviations of the discrimination factors. For example, dermis had the
largest inherent variation among the four tissue types as well as the largest standard
deviations in the estimates of discrimination values.
Consistent differences in 15N values have been observed for the same tissues
across a variety of species, likely because of different metabolic properties that are
used to create and maintain these tissues (Caut et al. 2009). Such differences might be
caused by the amino acid content of each tissue (Martínez 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 15N values among amino acids. For example, in mammals, Δ15N
values of plasma > hair > red blood cells (Caut et al. 2009), similar to the pattern
observed in this study with green turtle Δ15N values of serum > epidermis (a keratin-
40
based structure) > red blood cells. The mean difference between plasma/serum and
red blood cell Δ15N values was approximately .6 ‰ in mammals (Caut et al. 2009) and
was .7 ‰ in this study, averaged between adults and large juveniles.
Differences among tissues in 13C values can be influenced by amino acid
composition as well as lipid content, as lipids tend to be depleted in 13C (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 tissue.
Isotopic routing is another 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 differences rather than age, as animals with a
positive protein balance should have lower Δ15N values than animals that have a neutral
or negative protein balance (Martínez del Rio & Wolf 2005). Protein balance is
indicative of the efficiency of nitrogen deposition measured as the ratio between protein
assimilation and protein loss, and growing animals are expected to be in positive protein
balance (Martínez 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 supports the predictions by Martínez del Rio and Wolf (2005). Studies comparing
41
life stages or relative growth rates in other species have also reported patterns
corroborating this prediction in red foxes, Vulpes vulpes (Roth & Hobson 2000), Atlantic
salmon, 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
blood cells. The largest difference between Δ13C values for the same tissue occurred in
serum ( .93‰), 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 discrimination 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 discrimination
factors for juveniles in this study compared to those previously reported for juvenile
green turtles (Seminoff et al. 2006) because of the lower 13C and 15N values in the
diet of this study (Table 2). The method proposed by Caut et al. (2008, 2009) to apply a
diet-dependent discrimination factor may be appropriate for reconstructing sea turtle
42
diets through isotope mixing models. At this 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 factors. A positive trend between diet C:N ratios and Δ15N 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). Consistent
with the pattern observed by Robbins et al. (2005), the higher diet C:N ratio
corresponded to a higher Δ15N value. Yet further investigation of this pattern through
varied diets in a single mammalian species yielded no relationship between C:N ratios
and Δ15N values (Robbins et al. 2010). Rather, complementarity of amino acids and
diets composed of a mixture of items may contribute to variation in Δ15N values
(Robbins et al. 2010).
In comparison with other sea turtle species, the Δ15N 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 differences. The juveniles in this study were
larger and likely had reduced growth rates, which would lead to larger Δ15N values
(Martínez del Rio & Wolf 2005).
The carbon discrimination factors were more variable among the sea turtle
species. The largest Δ13C 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
43
comparisons between potential lipid content, as C:N ratios were not measured in 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. Inherent 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 underscoring 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 previous studies in sea turtles may be
attributable to differences in diet and growth rate. Understanding the processes that
influence isotopic discrimination and variance is fundamental to studies using stable
isotope analysis to investigate foraging, behavior, and ecological roles of wild
populations.
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 parameterizations.
*
45
Table 2-2. Mean 13C and 15N values (‰) and variance for each of the four tissues in both life stages from this study and other juvenile sea turtle tissues at isotopic equilibrium reported from the literature.
C. mydas (this study) C. mydasa Dermochelys coriaceab
13C (var)
15N (var) 13C (var)
15N (var) 13C (var)
15N (var)
Diet
Diet (lipid extracted)
-23.05 (0.29)
2.49 (0.05)
-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 (0.04)* 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 13C and 15N 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 on the same line. (Var = inherent variation)
aSeminoff et al. 2006
bSeminoff et al. 2009
*lipid extracted tissue
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 EPI
– 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
–
ΔBIC values >2 are considered significantly different (Raftery, 1995). Comparisons that have no biological meaning are not included. (EPI = epidermis, DERM = dermis, SER = serum, RBC = red blood cells, Juv=juvenile.)
* positive evidence for a difference between groups (ΔBIC values > 2 but < 6) ** strong or very strong evidence for a difference between the pair (ΔBIC values > 6)
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
–
ΔBIC values >2 are considered significantly different (Raftery, 1995). Comparisons that have no biological meaning are not included. (EPI = epidermis, DERM = dermis, SER = serum, RBC = red blood cells, Juv=juvenile.)
* positive evidence for a difference between groups (ΔBIC values > 2 but < 6) ** strong or very strong evidence for a difference between the pair (ΔBIC values > 6)
48
Table 2-5. Discrimination factors (Δ13C and Δ15N) 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 juvenilesa
Dermochelys coriacea juvenilesb
Caretta caretta hatchlingsc
Caretta caretta juvenilesc
Sample size 30 40 8 7 12 12
Δ13C
Epidermis 1.62 ± 0.61 1.87 ± 0.56 0.17* ± 0.08 2.26* ± 0.61 2.62† ± .34 . † ± . 7
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 ± 0.73 1.53 ± 0.17
Δ15N
Epidermis 4.04 ± 0.44 3.77 ± 0.40 2.80* ± 0.31 1.85* ± 0.50 .65† ± . 2 .6 † ± . 7
Dermis 4.93 ± 0.59 4.15 ± 0.47 – – – –
Serum/plasma 4.17 ± 0.41 4.06 ± 0.37 2.92 ± 0.08 2.86 ± 0.82 0.32 ± 0.09 1.50 ± 0.17
Red blood cells 2.48 ± 0.35 2.36 ± 0.37 0.22 ± 0.08 1.49 ± 0.76 -0.25 ± 0.30 0.16 ±0.08
All values are reported as means ± SD (‰). The discrimination factors in 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.
aSeminoff et al. 2006
bSeminoff et al. 2009
cReich et al. 2008
*lipid extracted tissue and diet †lipid extracted tissue
49
Figure 2-1. Results of three models using the first parameterization in which mean and
variance are estimated. Solid symbols represent mean 13C and 15N values, whereas open symbols represent individual measurements (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.)
-25 -24 -23 -22 -21 -20 -19 -18
4
5
6
7
8
9
d13
C (‰)
d1
5N
(‰
)
A
50
Figure 2-1 Continued.
-25 -24 -23 -22 -21 -20 -19 -18
4
5
6
7
8
9
d13
C (‰)
d1
5N
(‰
)
Adult
Juvenile
-25 -24 -23 -22 -21 -20 -19 -18
4
5
6
7
8
9
d13
C (‰)
d1
5N
(‰
)
Epidermis
Dermis
Serum
RBC
B
C
51
Figure 2-2. Results from the fourth hypothesis using the first parameterization, depicted
separately to highlight the eight groups. Solid symbols represent mean 13C
and 15N values, whereas open symbols represent individual measurements (see figure legend). Bivariate 95% confidence ellipses are drawn for adults (solid lines) and juveniles (dashed lines). Data are grouped by life stage and tissue type. (EPI = epidermis, DERM = dermis, SER = serum, RBC = red blood cells, Juv = juvenile.)
-25 -24 -23 -22 -21 -20 -19 -18
4
5
6
7
8
9
d13
C (‰)
d1
5N
(‰
)Adult EPI
Adult DERM
Adult SER
Adult RBC
Juv EPI
Juv DERM
Juv SER
Juv RBC
52
Figure 2-3. Comparison of isotopic variation in epidermis samples from 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).
-20 -15 -10 -5
0
2
4
6
d13
C (‰)
d1
5N
(‰
)
Captive (this study)
Wild (Inagua, Bahamas)
53
CHAPTER 3 TROPHIC ECOLOGY OF A GREEN TURTLE BREEDING 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 and 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 foraging 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 recruit 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 al. 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.
54
Previous analyses of stomach and feces content indicate that Greater Caribbean
green turtles are herbivorous with a diet composed predominately of seagrasses and/or
algae (reviewed in Bjorndal 1997a). Small amounts of animal matter, primarily 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 result 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 (Troëng et al.
2005).
Stable isotope analysis has become increasingly advantageous for revealing
resource use patterns in highly migratory marine vertebrates (Rubenstein & 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, Martínez del Rio et al. 2009a, Navarro et al. 2011). As a proxy for the ecological
niche, the isotopic 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).
55
Carbon isotope values (13C) 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 (15N) have been used as bionomic indicators that
reflect an organism’s trophic position (DeNiro & Epstein 1978, 1981, Post 2002).
However, these distinctions are not always clear, as many other factors can influence
13C and 15N 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,
O’Malley et al. 2 2, Pajuelo et al. 2 2, Seminoff et al. 2 2).
Values of 13C and 15N vary naturally with location as a result 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. 2010). 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 features 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 variations in bulk (total tissue) 15N values are a result of differences
in baseline 15N values or differences in trophic position (e.g., herbivory vs. carnivory).
Previous studies using AA-CSIA have quantified trophic levels of marine organisms
without having to characterize baseline 15N values (Popp et al. 2007, Hannides et al.
2009, Dale et al. 2011, Seminoff et al. 2012), which is particularly useful for systems in
56
which 15N values of primary producers vary spatially or temporally. This method relies
on the 15N values of two types of amino acids. “Trophic” amino acids (e.g., alanine,
glutamic acid, leucine, sensu Popp et al. 2007) are enriched in 15N relative to prey
presumably due to transamination and deamination reactions that cleave the carbon-
nitrogen bond (Chikaraishi et al. 2007). “Source” amino acids (e.g., glycine,
phenylalanine, sensu Popp et al. 2007) remain relatively unchanged in their nitrogen
isotope composition due to an absence or reduced metabolic processes that affect C-N
bonds (Chikaraishi et al. 2007).
I assess the trophic ecology and foraging ground distribution of green 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 the largest
rookery of green turtles in the Atlantic by an order of magnitude (Chaloupka et al. 2008).
Recoveries of over 4,600 flipper tags applied to individuals nesting at Tortuguero
indicate that these turtles travel throughout the Greater Caribbean from the Florida Keys
to Northern Brazil (Troëng et al. 2005). The large majority of flipper tag returns come
from seagrass beds of Nicaragua (Troëng 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
nesting 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 component of
57
Caribbean green turtle diets (Bjorndal 1997a). I further define baseline and trophic
contributions to bulk epidermis nitrogen isotopic composition of the nesting population
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 Collection and Preparation
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
( AAN), Nicaragua; Pearl Cays and Man O’ War/Tyra Cays area, South Atlantic
Autonomous Region (RAAS) Nicaragua; and St. Joseph Bay, Florida, USA (Figure 3-1).
Only adult females were sampled at the nesting 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 conversions to CCL were 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 sampled. For the remaining 151 turtles, CCL values
were derived from curved plastron 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, r2 = 0.84) (Lagueux and Campbell, unpubl. data). For Inagua,
58
direct CCL measurements were available for 42 of the individuals 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, r2 = 0.99) (Bjorndal 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 turnover time (Reich et al. 2008), it was assumed 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, en route to slaughter, or from stranded cold-
stunned 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 occurs
(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 oceanic foraging grounds were
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 carapace 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 60°C 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 100°C 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 (Figure 3-1, Table 3-2). Epiphytes 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 seagrass blades were dried in the laboratory at
60°C for 24 hours and ground to <1 mm in a Wiley Mill.
Sample Analyses
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) interfaced via a
ConFlo III to a DeltaPlus XL isotope ratio mass spectrometer (ThermoFisher Scientific).
Delta notation was used to express stable isotope abundances, defined as parts per
thousand (‰) relative to the standard
60
= (
- ) (3-1)
where Rsample and Rstandard are the corresponding ratios of heavy to light isotopes
(13C/12C and 15N/14N) in the sample and international standard, respectively. Vienna
Pee Dee Belemnite was used as the standard for 13C and atmospheric N2 for 15N. The
reference material USGS40 (L-glutamic acid) was used to normalize all results. The
standard deviation of the reference material was 0.13‰ for both 13C and 15N values (n
= 58). Repeated 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 0.12‰ for 13C values and 0.18‰ for 15N 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 turtles 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 15N values observed in the nesting
population (solid triangles Figure 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 derivatized to produce trifluoroacetic amino acid
esters using methods previously described (Macko et al. 1997, Popp et al. 2007).
Nitrogen isotopic compositions 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 described by Dale et al. (2011) and
Hannides et al. (2009). Internal reference materials, norleucine and aminoadipic acid,
61
were used to normalize measured 15N values. Each sample was analyzed in triplicate,
and data are presented as the mean of three analyses. Standard deviations for all
amino acids averaged .6‰ (range: 0.2 – 1.1‰).
Turtle Trophic Position
The fractional trophic positions (TP) of green turtles were based on the
relationship between nitrogen isotopic compositions of “trophic” (TrAA) and “source”
(SrcAA) amino acids using two variations of the equation proposed by Chikaraishi et al.
(2009). First, turtle TP was calculated using nitrogen isotopic compositions of glutamic
acid (glu) and phenylalanine (phe) representing “trophic” and “source” amino acids,
respectively:
TPglu/phe= (( 5Nglu- 5Nphe) glu-phe
T Fglu-phe
) (3-2)
where glu-phe is the difference between glu and phe in the primary producer, which
has been shown consistently to be -3.4‰ for aquatic primary producers (Chikaraishi et
al. 2009, 2010). However, here I demonstrate that seagrass amino acid biosynthesis is
more similar to that of C3 plants rather than macroalgae and phytoplankton (see
Results), and hence, for the purposes of this study a glu-phe value of 8.4‰ (C3 plants;
Chikaraishi et al. 2010) was used to calculate turtle TPglu/phe. The trophic enrichment
factor (TEF) is the expected enrichment in 15N with each trophic step for TrAAs and
SrcAAs (Chikaraishi et al. 2010) as calculated by:
T Fglu-phe= 5Nglu(consumer-diet)-
5Nphe(consumer-diet) (3-3)
Controlled feeding studies using herbivorous zooplankton and young carnivorous
fish have consistently yielded TEFglu-phe = 7.6‰ (Chikaraishi et al. 2009), and here this
value was used to calculate turtle TPglu-phe.
62
Second, turtle TP was calculated using a combination of all available TrAAs and
SrcAAs:
TPTr/Src= (( 5NTr-
5NSrc) Tr-Src
T FTr-Src
) 3-4
where 15NTr is the weighted mean 15N value of turtle TrAAs alanine (ala), leucine
(leu), aspartic acid (asp) and glutamic acid (glu), and 15NSrc is the weighted mean of
SrcAAs phenylalanine (phe), serine (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 they 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
data on which to base arginine or TEF values, so it was not included in TPTr/Src
calculations. For the purposes of Equation 4-4, Tr-Src was calculated as the difference
between weighted means for 15NTr and 15NSrc in seagrass (this study) and C3 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 C3 plant amino acid nitrogen isotopic composition (Figure 3-3) these
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 tyr were derived from
feeding study results (McClelland & Montoya 2002) to yield TEFTr-Src = 4.2 ± 0.2. In
63
both forms of the TP calculations, the error associated with each component of the
equation was propagated to determine TP error (Dale et al. 2011).
Data Analysis
All statistics were performed 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 13C–15N 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 area. 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
“typical” members in a population (Layman et al. 2012). I provide area estimates using
both metrics, but I only plot convex hulls because I am interested in extreme values in
both the nesting and foraging populations.
Results
The nesting population at Tortuguero exhibited a wide range in bulk epidermis
13C and 15N values (Figure 3-2A). Estimates of Bayesian ellipse area for each
foraging site resulted in slightly different size rankings than the convex hull area
method, but both measurements of the isotopic niche—convex hull area and Bayesian
64
ellipse area—generated smaller niche areas for each foraging aggregation than for the
nesting population (Table 3-1, Figure 3-2B).
Seagrass samples obtained from three foraging sites had a wide range of bulk
13C and 15N values (Table 3-2, Figure 3-2C). The range in 15N values (4.4‰) among
sites was larger than that in 13C values (2.3‰) (Table 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 were included from the literature (13C range = 7.8‰ and 15N
range = 6.3‰) (Table 3-3, Figure 3-2C). These ranges in the primary producer across
the Caribbean were nearly as large as those of the Tortuguero nesting population.
Seagrass nitrogen isotope fractionation patterns in amino acids were found to be
more similar to that of terrestrial C3 plants than to other aquatic primary producers such
as macroalgae, phytoplankton, and cyanobacteria (Figure 3-3). TPglu/phe (calculated
using only glu and phe) ranged from 1.7 to 2.1 when the -value for C3 plants was used
(Table 3-4; Figure 3-4). TPTr/Src (calculated using weighted means of all available TrAAs
and SrcAAs) yielded similar results to that of TPglu/phe, but with greater variability (Table
3-4; Figure 3-4). The 15N values of bulk epidermis and phe, a SrcAA, showed a
significant positive relationship (Figure 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 15N values fall inside the RAAN isotopic niche, and AA-CSIA data indicate
65
herbivorous feeding. Therefore, interpretations made from isotopic data for these
individuals were consistent with satellite tracking information.
Discussion
Interpreting the Isotopic Niche
Multiple lines of evidence indicate that the Tortuguero green turtle population is
composed of herbivores that feed over a wide geographic range of neritic habitats with
differences in stable isotope compositions of the primary producers. The wide range in
bulk 13C and 15N values of the nesting population can be explained by distinct isotopic
niches at foraging grounds, thus supporting 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 nesting 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, Meylan 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 15N values of
turtles from the nesting population. Despite a wide range of 15N values, the close
relationship between turtle bulk epidermis 15N and phe 15N values and similar TPglu/phe
estimates indicate that turtles are feeding at a similar trophic position across their
geographic range. Combining all available SrcAA and TrAA data to calculate TPTr/Src
66
demonstrates similar (though more variable) results. I conclude TP is most robustly
estimated by comparing nitrogen isotopic compositions of glu and phe, in concurrence
with results of Chikaraishi et al. (2009).
I found that the -value appropriate for seagrass is equal to that of C3 terrestrial
plants and very different from other aquatic primary producers. This is not surprising,
given seagrasses are descendant from terrestrial angiosperms and use a C3 pathway of
photosynthesis (Hemminga & Mateo 1996, Waycott et al. 2006). Trophic position
estimates were meaningless when using the -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 15N values of 6.3‰ in the nesting population
could represent two or more trophic levels (Post 2002), using available nitrogen isotope
discrimination factors for epidermis in green turtles (2.8 - 4. ‰; Seminoff et al. 2 6,
Chapter 2). In addition, lower 13C values are found in oceanic / pelagic habitats
compared to coastal / benthic habitats (Rubenstein & Hobson 2004). A subset of the
nesting population exhibits the combination of low 13C values and high 15N 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
67
isotope analysis, stomach contents, satellite tracking, and video 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 of 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 population size that is far from carrying capacity (Bjorndal & Jackson 2003).
Assessing the isotopic 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 Population Connectivity with the Isotopic Niche
Extensive commercial take of large juvenile and adult green turtles occurs in
Nicaragua (approximately 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 Tortuguero (Troëng et al. 2005), but estimates of the
proportion of this nesting population that come from foraging grounds in Nicaragua vary.
Understanding population interconnectivity is important for population modeling and
management, particularly in Nicaragua, where green turtle take is extremely high.
68
I explore the isotopic niche as an alternative method to assess the contribution 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 nitrogen
isotopic characteristics (Figure 3-2B).
Other methods have been used to estimate 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, Troëng 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 foraging
grounds (Sea Turtle Conservancy 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 estimated 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 and may be useful
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.
Outcomes
The Tortuguero nesting population is herbivorous and feeds over a wide
geographic range as indicated by seagrass stable isotope 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 15N 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 15N values. Therefore, I caution that bulk tissue
stable isotope values of a highly dispersed 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.
70
Table 3-1. Number of green turtles, size range, and year sampled at each of the five foraging grounds and the nesting beach location (Tortuguero).
Site name Country Number of individuals sampled
Size range CCL (cm)
Year sampled
Convex hull area
Bayesian ellipse area
Mean
13C (‰)
(min, max)
Mean
15N (‰)
(min, max)
Inagua Bahamas 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)
RAAS 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.
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 13C (‰) (min, max)
Mean 15N (‰) (min, max)
Union Creek, Great Inagua
Bahamas 3 2002 -6.64 (-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.
72
Table 3-3. Mean and SE of 13C and 15N values of Thalassia testudium analyzed in this study and collected from the literature for sites in the Greater Caribbean.
Site ID
13C (‰)
SE
13C
15N (‰)
SE
15N
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 Florida Keys Nat’l Marine Sanctuary, USA (Anderson & Fourqurean 2003)
6 -8.44 0.33 2.15 0.19 12 Florida Keys Nat’l Marine Sanctuary, USA (Anderson & Fourqurean 2003)
7 -10.41 0.38 1.14 0.2 12 Florida Keys Nat’l Marine Sanctuary, USA (Anderson & Fourqurean 2003)
8 -7.7 0.16 1.7 0.24 12 Florida Keys Nat’l Marine Sanctuary, USA (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, Florida, 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.
73
Table 3-4. Bulk tissue and amino acid 15N values of Tortuguero green turtle epidermis and seagrass (Thalassia testudinum).
Green turtles Seagrass
86070 99251 104857 113826 4 3† 4 28† 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 –
Val 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 –
74
Table 3-4 Continued.
Green turtles Seagrass
86070 99251 104857 113826 4 3† 4 28† WCC SC MC LR UC
TPglu/phe 1.7 1.9 2.1 2.0 1.8 2.0 – – – – –
TPTr/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 the 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 (TPglu/phe) or a combination of several “trophic” and “source” amino acids (TPTr/Src). *amino acids used in trophic position calculations †turtles were satellite tracked
75
Figure 3-1. Map of five foraging grounds (circles) and one nesting beach (star) where green turtles were sampled. Thalassia 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/).
76
Figure 3-2. Bulk tissue 13C and 15N values in green turtles and seagrass. A) Green 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 also satellite tracked. B) Green turtle epidermis at five foraging sites and one nesting beach (Tortuguero). Convex hulls represent the isotopic niche area for each population. C) Seagrass (Thalassia testudinum) samples from three green turtle foraging sites (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
Cc
77
Figure 3-3. Difference in 15N values between each amino acid and phenylalanine
( 15NAA-Phe) for Thalassia testudinum seagrass (this study), terrestrial C3 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 C3 plants than other aquatic primary producers. The relationship
between seagrass and C3 plants 15NAA-Phe values is significant for the eight
AAs for which data are available in both groups (p< . ; 15NAA-Phe (seagrass)
= 15NAA-Phe (C3) * 0.90(±0.11) + 1.3(±1.4); r2 = 0.91)
Amino acids
-30
-20
-10
0
10
D1
5N
AA
-p
he (‰
)
AlaGly
ValLe
uIle P
roSer
Glu
TyrLy
sArg
Asp Thr
Seagrass
C3 Plants
Aquatic Plants
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) (TPglu/phe) or with a combination of “trophic” and “source” amino acids (TPTr/Src) (see Methods). The horizontal line at 2 indicates the expected trophic position of an herbivore. Bars indicate ± 1 SD.
79
Figure 3-5. The relationship between the bulk epidermis 15N and phe 15N values in green turtle epidermis is significant (n = 6, F = 19.3, r2 = 0.78, p = 0.012).
80
CHAPTER 4 INDIVIDUAL SPECIALISTS IN A GENERALIST POPULATION: RESULTS FROM A
LONG-TERM STABLE ISOTOPE SERIES
Introduction
Hutchinson’s (1957) conceptualization 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 (Bolnick 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, Newsome 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 defined here as
the integration of diet, habitat, and geographic location) of individuals over time.
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
(Figure 4-1A), both individual and population isotopic niche widths are narrow. In the
first generalist population (Figure 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 wide isotopic niche space. In the second
generalist population (Figure 4-1C), 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 Figures 4-1A
and 4-1C would exhibit a cyclic pattern if 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 baselines (Reich et al. 2010, Pajuelo et al.
2012). I examine long-term consistency in resource use of a nesting loggerhead
population through stable isotope analysis of nitrogen and carbon in scute layers to
distinguish between the two types of generalist populations. Given the generalist nature
82
at the population level, my objective is to reveal the individual patterns of resource use
in loggerheads to determine if the population is composed of individual specialists or
generalists.
Materials and Methods
Scute Sampling and Analysis
Scute samples were taken with sterile 6-mm biopsy punches from 15 adult female
loggerheads (curved 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 posterior 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 anterior point at midline to the posterior notch at midline between
the supracaudal scutes (Bolten 1999).
Scutes were preserved in 70% ethanol after collection for approximately the same
time period, and each sample was rinsed clean in deionized water before drying at 60°C
for 24 hours. After lipid extraction with petroleum ether using an accelerated solvent
extractor, scutes were microsampled in 50-μm layers to provide sufficient sample for
stable isotope analysis using a carbide end mill with x, y, and z axis controls to a
precision of μm. The number of 5 -μm layers in a sample ranged from eight to 22.
Samples of 500-6 μg from each layer were combusted in an 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
83
Geological Sciences at the University of Florida, Gainesville, Florida. Delta () notation
is used to express all stable isotope ratios relative to the standard in parts per thousand
(‰) as follows:
= ( sample
standard- ) (4-1)
where Rsample and Rstandard are the corresponding ratios of heavy to light isotopes
(13C/12C and15N/14N) in the sample and international standard, respectively. Standards
were Vienna Pee Dee Belemnite (VPDB) for 13C and atmospheric N2 for 15N. The
reference material USGS40 (L-glutamic acid) was used as a calibration standard in all
runs. The standard deviation of the reference material was . and . 2‰ for 13C and
15N, respectively (n = 37). Repeated measurements 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 reference material was . 7 and . 3‰ for 13C
and 15N, respectively (n = 18). One anomalous layer (out of 196) was excluded from
analysis because insufficient sample was available to reanalyze it. The excluded point is
indicated by the dashed line in Figure 4-2.
Variation in 15N and 13C was analyzed using MANOVA with the Wilks’ lambda
test. Protected ANOVAs were used to compare variation in 15N or 13C within and
among turtles. Statistical analyses were performed with S-Plus software (version 8.1;
TIBCO Spotfire Software, Inc.) with α = . 5.
Estimation of Scute Age
I estimated the time required for scute to grow 5 μm to calculate the duration
represented in an entire scute sample. Scute turnover was estimated in four steps from
84
the carbon incorporation 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 to find the time required for 5 μm of scute
growth.
Step 1: Isotopic incorporation rate in juvenile loggerheads excluding growth.
The fractional rate of isotopic incorporation (kst) 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 (kgt) and the rate of catabolic degradation (kdt).
kst = kgt + kdt
The isotopic incorporation was attributed to catabolic degradation alone by setting
kst = kdt, 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
kdt = 0.013 day-1 for carbon.
Step 2: Scaling to adult body mass using -1/4 power. There is a two orders 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 (Martínez del Rio et al. 2009b). There is evidence that this
turnover rate scales with body mass to the -1/4 power (Carleton & Martínez del Rio
2005, Bauchinger & McWilliams 2009). Therefore, the value of catabolic turnover (kdt)
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 kdt = 0.0045.
85
Mass1 = 1.7 kg kdt1 = 0.013 day-1
Mass2 = 115 kg kdt2 = 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)/kst, and after four half-lives, turnover 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 13C values of one individual that occurred over
several layers (Figure 4-3). In that resource shift, turnover is achieved after three
layers. If it is assumed that the shift is abrupt and complete, it follows that each 5 μm
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 μm, thus the time in
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 were calculated from an allometric
relationship between isotopic turnover and body mass that has been demonstrated in
endotherms (Bauchinger & McWilliams 2009). Because these methods do not account
86
for differences in temperature, it is possible that the turnover time in these ectotherms is
an underestimate (Gillooly et al. 2001).
Results
One 50-μm layer of loggerhead scute was estimated to represent 0.6 years. The
scute samples range from 4 to μm in depth, and thus, the time interval in the
entire scute record ranges from 4 to 12 years (median 8).
Individuals exhibit high consistency in both 15N and 13C (Figure 4-2), and the
mean range of individuals is much smaller than that of the population for nitrogen and
carbon (Table 4-1). Individual patterns in resource use in both 15N and 13C combined
(Figure 4-4) reveal individual consistency (MANOVA, F = 437, p<0.001). Based on
ANOVAs, variation within individuals (<7% of total variation) was less than that among
individuals (Table 4-2).
Discussion
Loggerhead scute samples may contain up to 12 years of resource use history,
providing a lengthy record from which to investigate patterns in a long-lived species. To
my knowledge, this study reports the longest record of resource use history obtained
from living individuals.
Comparison of long-term scute records (Figure 4-2) with isotopic scenarios in the
conceptual model (Figure 4-1) reveals 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 population in
which to forage, individuals utilize only a limited fraction of the available isotopic niche
space (Figure 4-4).
87
In this study, specialization is not limited to a diet consisting of a single prey item,
but the observed isotopic specialization results from a consistent mixture of prey,
habitat, and geographic location, which cannot be separated 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 foraging (Barnes et al. 2008), it is
unlikely this would result in the wide isotopic range observed.
The large population range in 15N values (9. ‰) could be indicative 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 reflective 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 13C values ( .5‰)
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 13C.
The gap in 13C values between -12.5 and - 4.5‰ (Figure 4-2B) represents the
division between two foraging groups identified by Reich et al. (2010). The groups
could represent two general habitat use patterns that result from food webs with
different 13C values at the base of the food web as a consequence of an isotopic
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 different 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. Resource use heterogeneity, indicated by the broad
population isotopic niche width and narrow individual niche widths, reduces 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 within-individual variation (<7%, Table
4-1). In comparison, a recent study of individual specialization in sea otters, 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 individual 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 possess 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
89
location and identify the major component contributing to the high degree of individual
specialization that was observed.
90
Table 4-1. Minimum, maximum, and mean ranges of 15N and 13C for individual scute records (n = 15).
Minimum range (‰)
Maximum range (‰)
Mean range (±SD) (‰)
Population range (‰)
15N 0.33 2.42 0.93 (±0.66) 9.03
13C 0.36 3.23 1.26 (±0.65) 10.45
The population range is the difference between the maximum and minimum values for all individuals.
91
Table 4-2. ANOVAs indicate significant differences between the means of individuals.
SS among SS within F p-value
15N 1251.7 89.4 533.2 <0.001
13C 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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(a) Specialist population - specialist individuals
(b) Generalist population - generalist individuals
(c) Generalist population - specialist individuals
12
8
4
12
8
4
12
8
4
1 2 3 4 5
Time
Reso
urc
e u
se (
15N
‰)
Figure 4-1. Conceptual model of three population patterns of nitrogen stable isotope
values representing resource use through time. Arrows track individuals, and
each circle represents the 15N value 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.
93
Figure 4-2. Stable isotope values in successive layers of scute from 15 loggerheads.
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 sample for analysis. The number of layers reflects the thickness of the sample.
0 200 400 600 800 1000
2
4
6
8
10
12
1
5N
(‰)
0 200 400 600 800 1000
Distance from ventral surface (µm)
-19
-17
-15
-13
-11
-9
1
3C
(‰
)
A
B
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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 from 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 axes as Figure 4-2 for ease of comparison.
0 200 400 600 800 1000
Distance from ventral surface (µm)
-18
-16
-14
-12
-10
13C
(‰)
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Figure 4-4. 13C and 15N biplot for sequence of scute layers. Symbols represent the same individuals as in Figure 4-2.
-20 -18 -16 -14 -12 -10
13C (‰)
2
4
6
8
10
12
15N
(‰)
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CHAPTER 5 TEMPORAL CONSISTENCY AND INDIVIDUAL SPECIALIZATION IN RESOURCE
USE BY GREEN TURTLES IN SUCCESSIVE 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 specialization (Bolnick et al. 2003, Araújo et al. 2011).
This phenomenon, in which individuals use a narrow subset of the population’s resource
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
resources—represent the other extreme. Many 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 specialization 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 substantially reduced niche
compared to that of the population. I define these terms to distinguish between two
fundamentally different concepts that drove this study.
Stable isotope analysis is one strategy that has been employed to examine
consistency in resource use, specialization, and trophic niche breadth (Jaeger et al.
2010a, Codron et al. 2012, Fink et al. in press). Carbon and nitrogen stable isotope
values assimilated through the diet can reflect the ecological niche of a consumer, as
97
the values are determined by trophic position and habitat use. Because both habitat
and diet influence on stable isotope values, I employ the term “resource use” to reflect
the integration of these two factors in the foraging history of the animal. If stomach
content analysis is used to determine whether resource use is temporally consistent,
individuals must be sampled repeatedly through time. Alternatively, stable isotope
analysis of tissues that remain inert after synthesis provides a time series of resource-
use history. That is, a single tissue sample from an individual can be subsampled to
provide 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 scutes of sea turtle carapaces (Vander Zanden et al. 2010).
Inter- 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 outlined by Bearhop et al. (2004) to include temporal consistency
with predictions for repeated samples through time (Figure 5-1). I use an example with
15N values, though other measures representative of the ecological niche would also
be appropriate. A specialist 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 isotopic 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 resource use and display low intra-individual isotopic variance.
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High inter-individual variation contributes to the wide population isotopic niche (Figure 5-
1C). To differentiate among these population types, I used a metabolically inert tissue
that provides a diachronic stable isotope record from a single sample. Long-term diet
information could also have been obtained through repeated sampling of the same
individual or by using tissues that have different turnover rates and integrate different
time scales (Bearhop et al. 2004, MacNeill et al. 2005, Martínez 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, Araújo et al. 2011), and stable
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). Few
studies, however, have examined how temporal consistency and/or individual
specialization changes across life stages within a single species (Nshombo 1994,
Sword & Dopman 1999, Frédérich 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 green turtles use oceanic, or open ocean,
habitats during a life stage that has been termed the “lost years” because of the lack of
knowledge regarding diet and location (Carr 1987). Oceanic juveniles are omnivorous
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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 & Glor 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 Africa (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 they 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 addressed two objectives by comparison of green turtles in
successive life stages, i.e. oceanic juveniles, neritic juveniles, and adults. First, I
quantified 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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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 time (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 familiarization 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 juveniles 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 collected from 43 green turtles in two locations (Table 5-1).
Samples were collected from the posterior medial region of the second lateral scute
(see Reich 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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longer posterior marginal scutes (Bolten 1999). To standardize measurements to the
curved carapace length (CCL) used for adults, SCL measurements were converted to
CCL using a regression developed with 1421 juvenile green turtles from Union Creek
encompassing the size range of the sample population (CCL = 1.04*SCL – 0.35, R2 =
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 adult 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 midline (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% ethanol (Barrow et al. 2008).
Sample Preparation and Analysis
Scute samples were rinsed with deionized water and dried at 60°C for 24 hours.
Scutes from juvenile turtles were 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 correction (Post et al. 2007). I assumed green turtle scute is
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similar to loggerhead scute, and therefore, that lipid extraction would not significantly
alter the isotopic value of scute samples.
Each scute biopsy was glued to a glass slide, and successive 50-μm layers were
obtained using a carbide end mill. This interval was selected 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, lowest 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 XL isotope ratio
mass spectrometer (ThermoFisher Scientific). Delta notation is used to express stable
isotope abundances, defined as parts per thousand (‰) relative to the standard
= sample
standard- (5-1)
where Rsample and Rstandard are the corresponding ratios of heavy to light isotopes
(13C/12C and 15N/14N) in the sample and international standard, respectively. Vienna
Pee Dee Belemnite was used as the standard for 13C and atmospheric N2 for 15N. The
reference material USGS40 (L-glutamic acid) was used to normalize all results. The
standard deviation of the reference material was 0.20‰ for 13C (n = 53) and 0.15‰ for
15N 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 . ‰ for 13C values and . 7‰ for 15N values (n = 21).
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Scute Growth Rate
Scute growth rate was estimated by methods similar to those used by Vander
Zanden et al. (2010). Each 50-μm 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 juveniles 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 have 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 approximately 2.8 years. The 700-μm scute sample from this turtle captures a
complete oceanic-to-neritic shift (Figure 5-2A and B), and assuming all the sampled
scute tissue was deposited since the shift to the neritic zone, each layer represents
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 & Martínez 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 kg (Bjorndal &
Carr 1989). Therefore, by scaling the scute turnover time in juveniles to the appropriate
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adult body size, the estimated time period represented in each 50-μm layer of adult
scute is nearly twice that of the juveniles, or 148 days.
Data Analysis
The degree of individual specialization 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 individuals, 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 13C and 15N 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,
MSW=∑ ∑ (xij-x̅i)
2ji
(N-k) (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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MSB=∑ ∑ (x̅i-x̅)
2ji
(k- ) (5-3)
where i represents an individual, j represents a single scute layer, N is the total
number of observations, 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 performed using R® (R
Development Core Team 2011).
Results
Scute Records
Oceanic juveniles. Twenty-six juveniles caught in Union Creek during the
sampling period did not have flipper tags and were therefore assumed to be recent
recruits. Stable isotope patterns in their scutes (Figure 5-3) were used to identify
individuals that still contained a record of the oceanic phase. The oceanic stage is
characterized by low 13C values and high 15N values, whereas the neritic stage is
characterized by high 13C values and low 15N 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 (Figure 5-
3A and B). These were also the smallest of the 26 turtles with CCL measurements < 33
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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 reflecting the ontogenetic 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 individual specialization in the oceanic life stage (Table 5-1,
Figure 5-4A and B). The time period represented in these records encompasses 0.8 to
2.0 years.
Neritic juveniles. Fourteen juveniles that had previously been captured and
tagged in Union Creek were 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-μm 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 13C values and low 15N values (Figure 5-5A and B). The
majority of the turtles in this group 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 individual 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 (Figure 5-6A and B). There was
no significant difference between the number of 50-μm 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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Temporal Consistency and Individual Specialization
Serving as a metric for temporal consistency, the mean within-individual variance
(WIC) for 13C values decreased with increasing age, and adults were significantly more
consistent than oceanic juveniles (Figure 5-7A, Table 5-2). Mean within-individual
variance in 15N values was not significantly different between oceanic and neritic
juveniles but was significantly lower in adults than in either juvenile life stage (Figure 5-
7A, Table 5-2).
The degree of individual specialization (WIC/TNW) was approximated through the
ANOVA framework. All life stages had WIC/TNW values < 0.15, indicating individual
specialization occurs across ontogenetic life stages. The degree of individual
specialization was similar between the two juvenile life stages (Figure 5-7B), but neritic
and oceanic juveniles had WIC/TNW ratios that were significantly higher than adults for
both 13C and 15N 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 not
only have a records of resource use history from the same individuals that enable me to
quantify temporal consistency over time, but I can also examine these records relative
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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 help reveal ecological
interactions and community structure (Layman et al. 2007b, Nagelkerken et al. 2006).
An individual’s resource use is molded by complex interactions between available
resources and maximization 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 fledging
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. For instance, foraging 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 of
ontogeny on the degree of individual specialization is negligible, compared to the
influence of group density (Frédérich et al. 2010). In this study, I found that temporal
consistency and individual specialization in resource use of green turtles varied among
life stages.
Comparison Among Green Turtle Life Stages
Integrated 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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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 remain in a foraging region with
consistent isotopic values at the base of the food web, this could also contribute to the
temporal consistency observed in the oceanic life stage.
Low WIC/TNW values observed for all life stages indicate that individual
specialization occurs throughout ontogeny, so that all populations most resemble
generalist type B populations. The 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 13C values suggestive
of the shift. Using a previous characterization of oceanic and neritic isotopic patterns
(Reich et al. 2007), a 13C value of approximately - 2‰ separates oceanic from neritic
habitat use, with the latter group displaying values >- 2‰. After removing the turtle with
the complete shift, there were no other 13C values < - 2‰ in the neritic juvenile group.
I am confident that layers with 13C values >- 2‰ were not deposited in the oceanic
habitat, though some may represent the isotopic transition to the neritic habitat. These
trailing 13C values, as well as oscillations in 15N values, contributed to the higher
mean individual variance in this life stage in comparison to adults. Neritic juveniles also
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.
The observed variation in isotope values may be a consequence of 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 15N 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 digestibility
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. Whereas juvenile green turtles in a Florida lagoon do not
consume any animal matter (Mendonça 1983), juvenile green turtles in neritic areas of
the eastern Atlantic maintain more generalist or omnivorous foraging patterns, and do
not appear to become exclusive herbivores (Cardona et al. 2009). Once 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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Individual adult 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 observed 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 exhibited a
wide range in isotope 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 represents 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 migrate from multiple foraging grounds
across the Caribbean (Troëng 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
generalization in resource use observed at the population level is principally a
112
consequence of differences in foraging location, but that specialization among
individuals is caused by temporal consistency in diet at a given foraging area over a
period of 2-6 years. 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 individuals 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 bouts (Limpus et al. 1992,
Broderick et al. 2007).
The degree of individual 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 than do adult turtles originating from multiple foraging grounds,
or oceanic turtles that are likely using many foraging areas (M. López-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 periods 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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juvenile scute growth is also supported by the short time duration (38-172 days) that
satellite transmitters remain attached to juvenile sea turtles (Mansfield et al. in press)
compared to longer time periods (67-423 days) in adults (Troëng et al. 2005,
Blumenthal et al. 2006). Scute growth rate also appears to be species-dependent, as
the estimated time period represented in a 50-μm scute layer from adult green turtles is
shorter than that of adult loggerheads (Vander Zanden et al. 2010) Thus the maximum
time reflected in a whole scute sample from green turtles is shorter than that for
loggerheads.
External scute layers in marine turtles are lost by mechanical wear and sloughing,
and these layers are composed of the oldest tissue (Day et al. 2005). The loss of scute
tissue is somewhat irregular, and the precise mechanism of loss is unknown, but the
process allows 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 patterns 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 indicative 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 constant 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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highly influenced by the total niche width of the population. More information is needed
about whether individual differences among foraging sites (Chapter 3) or diet (Hatase et
al. 2006, Burkholder et al. 2011) have consequences for fitness or if nutritional history in
earlier life stages affects long-term survival and growth (Roark et al. 2009). As major
consumers within these marine ecosystems, it is important to recognize that temporal
consistency and degree of individual specialization in green turtles can vary with life
stage and that not all individuals are ecologically equivalent.
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Table 5-1. Scute samples were collected from three life stages of green turtles at two sampling locations.
Sampling location
Life stage Year Number of individuals
Range of layers
CCL min-max, mean (cm)
13C min-max, range (‰)
15N min-max, range (‰)
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 – 62.3 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 size 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 represented the transition between oceanic and neritic habitats.
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Table 5-2. Within individual contribution (WIC) and total niche width (TNW) approximated through the ANOVA framework among three life stages.
Life stage 13C WIC
13C TNW 15N WIC
15N TNW
Oceanic juveniles 0.95 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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Figure 5-1. Conceptual model of resource use and predicted patterns in nitrogen stable
isotope values. A) Specialist population. B) Generalist population type A composed of generalist individuals. C) Generalist population type B composed of specialist individuals. Arrows track individuals, and each circle
represents the 15N value for a layer of inert tissue, which reflects resource use. Modified from Vander Zanden et al. (2010).
(a) Specialist population
(b) Generalist population Type A
generalist individuals
(c) Generalist population Type B
specialist individuals
12
8
4
Re
so
urc
e u
se
(δ
15N ‰
)
12
8
4
1 2 3 4 5
Time
12
8
4
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Figure 5-2. Stable isotope values in successive 50 μm 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 time period represented in each 50 μm scute subsection.
A
B
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Figure 5-3. Stable isotope values in successive 50 μm 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 isotope 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 corresponds to older time periods in the turtle’s foraging history. Individuals are represented by unique symbols.
A B
C D
E F
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Figure 5-3 Continued.
G H
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Figure 5-4. Stable isotope values in successive 50 μm subsections of scute in 8
juvenile green turtles from Inagua, Bahamas, representing the oceanic life stage. A) Carbon isotope values. B) Nitrogen isotope values. Increasing distance from the lower surface of the scute sample corresponds to older time periods in the turtle’s foraging history. Individuals are represented by unique symbols. Some individual records do not begin at 50 μm, as only the layers that reflect the oceanic life stage are depicted.
A
B
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Figure 5-5. Stable isotope values in successive 50 μm 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. Increasing distance from the lower surface of the scute sample corresponds to older time periods in the turtle’s foraging history. Individuals are represented by unique symbols. The individual with the longest record, represented with a crossed diamond, was used to estimate the time period represented in each scute layer and was removed from statistical analysis.
A
B
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Figure 5-6. Stable isotope values in successive 50 μm 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 scute sample corresponds to older time periods in the turtle’s foraging history. Individuals are represented by unique symbols.
A
B
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Figure 5-7. Metrics to compare temporal consistency and individual specialization
among life stages: oceanic juveniles, neritic juveniles, and adult green turtles. A) Mean variation within individuals, or WIC, as calculated with MSW from the ANOVA framework. B) The degree 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 when 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
ab
b
125
separately for 13C and 15N values, and pairs that do not share letters are significantly different. See the text for acronym definitions.
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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 isotope analysis. In 1983, this methodology was first
applied to loggerhead sea turtles foraging along the US coast (Killingley & Lutcavage
1983). The 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, this technique has been increasing dramatically
in the field of sea turtle biology.
Stable isotope ratios of carbon and nitrogen in consumer tissue reflect both diet
and habitat. One major application of stable isotope analysis has been the assessment
of the proportion of potential diet items through mixing 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 better 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
between a consumer’s diet and tissue, 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 later reviews (Vander Zanden & Rasmussen 2001, Post 2002).
Further research has revealed that discrimination factors may be more complex and
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variable than originally thought, with potential influence of tissue type, diet, protein
quality, growth, and species (Martínez 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 diet and species differences. I have
found that the discrimination factors in green turtles are dependent on tissue, growth,
diet, and species. Therefore, this research contributes to creating more accurate diet
reconstructions in future research by providing appropriate discrimination factors.
Stable Isotopes Never Lie
Jim Ehleringer, a distinguished researcher in the field of stable isotope ecology
who directs an annual course on the subject, has stated that stable isotopes never lie,
but sometimes 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 meaningful conclusions regarding the biology of the organism of interest. After
accounting for discrimination between tissue and diet, the 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 omnivorous foraging patterns that
have been observed in other green turtle populations worldwide (Hatase et al. 2006,
Amorocho & Reina 2007, Arthur et al. 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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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). In the absence of further investigation, I could have erroneously
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 spatial variation at the base of the food web. That is, the ocean basin is not
geographically homogeneous 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 patterns.
The connectivity between nesting and foraging grounds has been of great interest
to sea turtle biologists. Because of 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 about 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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analysis to measure the migratory connectivity between nesting and foraging
populations.
Creatures of Habit
The time period represented in the stable isotope ratios of an organism’s tissue is
dependent upon the metabolic properties of the tissue, which affect the turnover time, or
replacement period. Some tissues are rapidly replaced such as plasma, whereas other
tissues such as skin and muscle, represent longer time periods. Finally, other tissues
such as hair, tusks, and feathers are inert after synthesis and may represent the longest
time periods, depending on the physical retention time of these 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.
Unfortunately, scute is not retained for the lifetime of the 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, including 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
developed 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 variation
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 data to
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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 through 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 patterns of loggerhead sea turtles revealed long-term consistency in
resource use through time, with isotopic differences among individuals, which prompted
me to conclude that the generalist population was composed of specialist individuals.
However, I was unable 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 satellite telemetry revealed that variation in the 13C and 15N
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 predominant biogeochemical processes in each region (Pajuelo et al.
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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
consistent 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 with 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 combination 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 resources used within a more
generalized population (Chapter 5). Again, the total population isotopic niche was
influenced by the variation in stable isotope values among foraging sites, and green
turtles appear to have high fidelity to foraging sites 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 strategy 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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originates from multiple foraging grounds, which necessitates caution when defining the
population niche width to determine the degree of individual specialization.
Although sea turtles in the same life stage may utilize many foraging grounds,
recognizing 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 identify 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 incorporates 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 between 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 Rica appear to subsidize the
terrestrial vegetation. Total percent nitrogen and 15N 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-
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 turtles 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 ecosystems 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 the diet, it
is also helpful to know the isotopic offset between a female sea turtle and her offspring.
Because there 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 information
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 those in the females
134
(Frankel et al. 2012). Calculating 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 reliability of stable isotope values. Other
studies have examined the effect of preservation methods on sea turtle tissues (Barrow
et al. 2008, Lemons 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
variation. These 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 loggerhead 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 with. It provides long-term
consecutive time intervals of resource use, and 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
135
samples. Thus, precise measurements of scute 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 likely 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). Loggerheads 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 tracking (Zbinden et al. 2011, Pajuelo et al. 2012), compound specific
stable isotope analysis (Seminoff et al. 2012, Chapter 3), stomach 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 isotope data to distinguish fine-scale
habitat and dietary information, yet the broad-scale patterns are tremendously useful.
Most sea turtle studies incorporating stable isotope analysis have utilized carbon and
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, vanadium)
136
may be useful for resolving differences that are otherwise indistinguishable (M. López-
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 the 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 these 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 marine and adjacent terrestrial
ecosystems can help to provide more meaningful goals for their conservation.
137
Figure 6-1. Consumers in each population have distinct diets, with three possible prey
types that vary in their stable isotope ratios. A specialist population feeds 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 types. With single point sampling of a tissue that represents a short time period, both generalist populations would have the same mean and variance in stable isotope values, making it is impossible to distinguish between the two. Adapted from Bearhop et al. (2004).
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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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 conservation program at a beach near
Tomatlán, 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 Undergraduate 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 her 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
implemented projects to promote girls’ education and construct latrines.
She worked briefly as an environmental educator for Nature’s Classroom before
beginning her PhD with Karen Bjorndal in the Department of Zoology at the 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 Ecology 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 Institutional Integration Graduate Student
Advisory Council, the Biology Graduate Student Association, and the Graduate Student
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Council. She has received a Florida Sea Turtle Grant to begin a postdoctoral position at
the University of Florida in the fall of 2012.
