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Energetics of the leatherback turtle, Dermochelys coriacea Jones, Timothy Todd 2009

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ENERGETICS OF THE LEATHERBACK TURTLE, DERMOCHELYS CORIACEA  by  Timothy Todd Jones  B.Sc., Florida Atlantic University, 2000 M.Sc., Florida Atlantic University, 2004  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  DOCTOR OF PHILOSOPHY  in  THE FACULTY OF GRADUATE STUDIES (Zoology)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)  April 2009 © Timothy Todd Jones, 2009  ABSTRACT I have quantified the energy requirements of leatherback turtles (Dermochelys coriacea) throughout development, and examined growth rates, resource requirement and availability, and anthropogenic threats from the commercial fishery. I demonstrated that the use of the doubly labeled water (DLW) method to determine field metabolic rate in marine turtles is constrained by low metabolic (MR) and high water turnover rates (Chapter 2). For fed and fasted turtles, water turnover rates were 9.57±1.33% and 6.14±0.65% TBW day-1 and MR (from respirometry) was 28.66±5.31 kJ kg-1 day-1 and 13.77±1.49 kJ kg-1 day-1, respectively. This led to isotope turnover (kd:ko) ratios of 0.91±0.02 for fed turtles and 1.08±0.16 for fasted turtles, producing negative MRs for fasted turtles. While I showed that for fed turtles the DLW method was consistent with respirometry the use of DLW in fasting turtles differed from respirometry by 440%. The fact that DLW does not work in certain situations is a rare finding that will be of broad interest in the field of energetics.  Having determined that the DLW method is constrained in marine turtles I then turned to rearing leatherbacks in the laboratory to measure growth (Chapter 3) and determine energy intake (Chapter 4). For the first time I was able to rear several leatherbacks from hatching to juveniles. Leatherbacks maintained an average growth rate of 31.9 ± 2.8 cm year-1 in straight carapace length (SCL) throughout the study period. The captive leatherbacks matched the length-mass relationship of wild juveniles and adults. A von Bertalanffy growth function (VBGF) predicted age-at-maturity for leatherbacks of 15.3 years. Bycatch data, supplemented with growth curve data, indicate that leatherbacks will reach the minimum length at which they are found interacting with fisheries (drift gill net and longline) in less than 3 years, suggesting they are ii  exposed to threats from marine fisheries for > 80 % of their life before maturity is attained. I estimated that the majority of the Pacific Ocean population of leatherbacks is made up of 2-6 year old juveniles (137,368 turtles) consuming 1.6 x 106 tonnes of jellyfish year-1. These turtles are restricted to warmer equatorial waters where primary productivity and, possibly, jellyfish abundance are low.  iii  TABLE OF CONTENTS ABSTRACT ............................................................................................................................. ii TABLE OF CONTENTS ............................................................................................................ iv LIST OF TABLES ..................................................................................................................... vi LIST OF FIGURES .................................................................................................................. vii LIST OF ABBREVIATIONS ....................................................................................................... ix ACKNOWLEDGEMENTS ......................................................................................................... xi CO-AUTHORSHIP STATEMENT ..............................................................................................xiii CHAPTER ONE: GENERAL INTRODUCTION ............................................................................. 1 OPENING BY ISHMAEL ..............................................................................................................................................1 INTRODUCTION ........................................................................................................................................................1 Phylogenetic history .............................................................................................................................................1 Life history ............................................................................................................................................................2 Bioenergetics of Leatherbacks: a crucial contribution to survival ........................................................................4 Thesis organization ..............................................................................................................................................8 Objectives .............................................................................................................................................................9 Chapter two: Validation of the Use of Doubly Labeled Water for Estimating Metabolic Rate in the Green Turtle (Chelonia mydas L.): a word of caution ............................................................................................................................. 9 Chapter three: Growth of captive leatherback turtles Dermochelys coriacea with inferences on growth in the wild: implication for fisheries induced population decline ........................................................................................................ 9 Chapter four: Population status and total biomass of Pacific leatherback turtles (Dermochelys coriacea) derived from growth and food conversion studies in captive leatherbacks ......................................................................................... 10  REFERENCES............................................................................................................................................................11  CHAPTER TWO: VALIDATION OF THE USE OF DOUBLY LABELED WATER FOR ESTIMATING METABOLIC RATE IN THE GREEN TURTLE (CHELONIA MYDAS L.): A WORD OF CAUTION1 ...... 17 INTRODUCTION ......................................................................................................................................................17 MATERIALS AND METHODS ...................................................................................................................................20 Animals...............................................................................................................................................................20 Experimental design ...........................................................................................................................................21 Respirometry ......................................................................................................................................................22 Doubly labeled water determinations ................................................................................................................24 Analysis of isotopic data.....................................................................................................................................25 RESULTS ..................................................................................................................................................................28 Respirometry ......................................................................................................................................................29 Doubly labeled water determinations ................................................................................................................29 DISCUSSION ............................................................................................................................................................31 CONCLUSIONS ........................................................................................................................................................39 REFERENCES............................................................................................................................................................46  iv  CHAPTER THREE: GROWTH OF CAPTIVE LEATHERBACK TURTLES DERMOCHELYS CORIACEA WITH INFERENCES ON GROWTH IN THE WILD: IMPLICATION FOR FISHERIES INDUCED POPULATION DECLINE2 ........................................................................................................ 51 INTRODUCTION ......................................................................................................................................................51 MATERIALS & METHODS ........................................................................................................................................54 Animal husbandry ..............................................................................................................................................54 Data collection and analysis ...............................................................................................................................56 Statistical analysis ..............................................................................................................................................58 RESULTS ..................................................................................................................................................................60 Length mass relationships ..................................................................................................................................60 Growth ...............................................................................................................................................................61 DISCUSSION ............................................................................................................................................................62 Length mass relationship ...................................................................................................................................62 Growth ...............................................................................................................................................................63 REFERENCES............................................................................................................................................................81  CHAPTER FOUR: POPULATION STATUS AND TOTAL BIOMASS OF PACIFIC LEATHERBACK TURTLES (DERMOCHELYS CORIACEA) DERIVED FROM GROWTH AND FOOD CONVERSION STUDIES IN CAPTIVE LEATHERBACKS3 .................................................................................. 88 INTRODUCTION ......................................................................................................................................................88 MATERIALS & METHODS ........................................................................................................................................90 Animal care ........................................................................................................................................................90 Diet .....................................................................................................................................................................91 Growth ...............................................................................................................................................................92 Food conversion and consumption .....................................................................................................................93 Metabolic rate and validation of food consumption model ...............................................................................95 Estimates of food consumption and biomass of the population ........................................................................96 Uncertainty and statistical analysis ...................................................................................................................98 RESULTS & DISCUSSION ..........................................................................................................................................99 Growth and food consumption ..........................................................................................................................99 Metabolic rate and validation of the model.....................................................................................................100 Mortality, food consumption and biomass for the Pacific population .............................................................101 REFERENCES..........................................................................................................................................................111  CHAPTER FIVE: GENERAL DISCUSSION ................................................................................ 120 CLOSING BY ISHMAEL ...........................................................................................................................................129 REFERENCES..........................................................................................................................................................131  APPENDIX A ...................................................................................................................... 137  v  LIST OF TABLES Table 2.1 Mass (fasted is the average between days 10 & 15; post-fast 21 days is 21 days from start of trial and so on), background isotope levels, injectate details, isotope dilution space, washout ratios, metabolic rate (DLW MRs from equation 7, see Methods), and water turnover rate for 6 green turtles used in DLW validation…………………..…41  Table 2.2 Body pool estimate (N) and DLW derived metabolic rate (MR) for the 7 equations listed in the methods section for the fed trials…………….…………………………42  Table 3.1 Weekly measurements of SCL and mass of 20 leatherback turtles………………….69  Table 3.2 SCL and mass-at-age of individual turtles raised in captivity……………………….70  Table 3.3 SCL and mass of 11 wild individual turtles (stranded or as bycatch)……………….71  Tables 3.4 Growth rate (ΔL/Δt) and mid-length (L1 + L2)/2 calculated from leatherback turtle growth data (Table 1) separated into 10 cm SCL data bins………………………….72  Table 3.5 Parameters from three growth models (von Bertalanffy, Gompertz, and logistic) for the length-at-age data in Table 3.2 and adult data from skeletochronology studies (Zug & Parham 1996; Avens et al. unpublished data)………………………..…… 73  Table 3.6 SCL of leatherback turtles bycaught in artisanal, drift gill net (DGN) and longline fisheries of the Pacific Ocean………………………………………………………..74  Table 4.1 Total number of hatchlings entering the Pacific Ocean each year calculated from nesting ecology data from the literature………………………………………….....105  vi  LIST OF FIGURES Figure 2.1  Isotopic enrichment values of the equilibration time course for deuterium (2H) and oxygen-18 (18O) of three green turtles (Chelonia mydas)……………………….43  Figure 2.2  Figure 2.2 Isotopic enrichment values for deuterium (2H) and oxygen-18 (18O) during the course of the DLW validation experiment for 6 green turtles (Chelonia mydas)……………………………………………………………………………44  Figure 2.3  Natural log of isotopic enrichment above background levels used for DLW metabolic measurements using the Multiple-Sample Approach………………...45  Figure 3.1  Log of length-mass relationships (log length-log mass) of leatherback turtles for (A) this study (Table 3.2)………………………………………………...………75 (B) juveniles from the wild (Table 3.3)………………………………………….76 (C) all available L-M data pairs………………………………………………….77  Figure 3.2  Growth functions for leatherback turtles (A) von Bertalanffy…………………..78 (B) Gompertz…………………………………………………………………….79 (C) logistic……………………………………………………………………….80  Figure 4.1  Log-log transformation of food conversion efficiency ‗K1‘ showing the best-fit curve with 95% confidence bands……………………………………………...106  Figure 4.2  The rate of food consumption as a function of age (F1,t), giving the food intake of a single leatherback at any age throughout its life-history………………..……107  vii  Figure 4.3  Metabolic rate (W kg-1) determined from food consumption plotted with metabolic rates of leatherback …………………………………………………108  Figure 4.4  Red arrows show location of the eastern and western Pacific leatherback nesting rookeries……………………..………………………………………………….109  Figure 4.5  Total Pacific leather population biomass (A); and consumption rates in tonnes of jellyfish per year for the entire Pacific leatherback population (B)………….....110  Figure 5.1  Sea-viewing Wide Field-of-View Sensor (SeaWiFS). Green areas indicate areas of high primary productivity (chlorophyll-a)…………………………………...130  viii  LIST OF ABBREVIATIONS AE  assimilation efficiency  °C  degrees Celsius  CCL  curved carapace length  DGN  drift gillnet  DLW  doubly labeled water  DM  dry mass  DSR  isotope dilution space ratio  FMR  field metabolic rate  IC  intra-coelemic  IDS  isotope dilution space  IUCN  world conservation union  kd  deuterium isotope washout  ko  oxygen-18 isotope washout  L∞  asymptotic length  L-M  length-mass  Lmin  minimum nesting length  MR  metabolic rate  MSA  multiple-sample approach  MSres  mean square residual  N  body water pool  rCO2  carbon dioxide production  RER  respiratory exchange ratio ix  rH2O  water turnover rate  RQ  respiratory quotient  SCL  straight carapace length  TBW  total body water  VBGF  von Bertalanffy growth function  x  ACKNOWLEDGEMENTS I start my acknowledgements, as I should, with a leatherback sized thank you to my advisor David R. Jones. Dave was one of the UBC ‗Big 4‘ in comparative physiology, and it is an honor to be part of his legacy. Dave gave me the academic freedom and monetary support to pursue my interest in sea turtle physiology; and along the way we had some incredible discussions, I learned some lessons, we saw 6 Seattle Seahawks football games, and drank plenty of beer together. Dave as your last PhD student, I can only hope that it was as memorable and pleasant for you as it was me!  I would like to thank my supervisory committee members: Bill Milsom, Trish Schulte, Colin Brauner and Jeff Richards who kept me on track and gave valuable critiques of my research. I thank Daniel Pauly for his advice and mentorship. I also have to thank Alison Barnes and Alice Liou the Graduate Secretaries during my term, I was one of those problem children but they kept smiling and always helped me out of any situation I found myself. I give special thanks to my lab mates: Amanda Southwood, Manu Gardner, Kim Borg, Manfred Enstipp and our surrogate lab mate Charles Darveau (my snowboarding partner). Charlie you taught me a lot about physiology – thank you. To Tippy my right-hand man in the field and surf partner. Left out from this list are Mervin Hastings and Brian Bostrom, words cannot describe the friendship we shared and the undying willingness to help each other out be it staying the night in the lab, hoisting turtles, or mid-night feeding and re-harnessing of break-away turtles. Mervin was a lab mate, roommate and friend and we spent many a time remembering how nice it is in Florida and the Caribbean (our respective homes). Merv and I managed to get Dave to pay for some incredible field seasons, we were at our best in flip-flops and tropical heat. Swerv left us with some classic xi  quotes, ―you‘re on my minutes‖ and ―I eat my bones‖, if you say these out loud do it as if you were Mr. T, that‘s Swerv. After Merv graduated Brian filled the void and showed me that you can dive, fish, sail and drink rum in British Columbia just like in Florida! BB is a true Beer Drinker, Gunslinger!!! I thank Colette Wabnitz, a fellow sea turtle biologist, if anyone reading this is doing a PhD I suggest you pair up with someone to graduate in the same term. Colette you have helped to keep my sanity and thank you for your invaluable edits and discussions. I also thank the numerous undergraduates for their enthusiasm in volunteering to care for the leatherback and green turtles. Chris Harvey-Clark has been an unending supporter of the leatherback project and makes a mean Yorkshire pudding! Art Vanderhorst and Sam Gopaul made life at South Campus pleasant – peanuts, milk, bread, and newspapers; they also were invaluable when it came to sea turtle emergency care. Sam taught me how to make curry, I will be forever grateful.  I thank O‘Brian, Forester, and Melville for allowing me to escape to a time and an occupation that I often dream about. I thank my parents Carole and Dennis for supporting me and their pure delight in my successes as well as my brothers Kirk and Troy. Scott, Scott and Brian – my best friends for the last 18 years – what else needs to be said! Finally I thank Ashley, for her love, support, and belief in me.  xii  CO-AUTHORSHIP STATEMENT Chapter One:  Portions of chapter one (Introduction) were reproduced or adapted from What makes marine turtles go: A review of metabolic rates and their consequences  Authors:  Bryan P. Wallace and T. Todd Jones  Date Accepted:  December 23, 2007  Journal:  Journal of Experimental Marine Biology and Ecology 2008 356: 8-24  Comments:  This study was a review co-written by BPW and TTJ as experts in the field.  Chapter Two:  Validation of the Use of Doubly Labeled Water for Estimating Metabolic Rate in the Green Turtle (Chelonia mydas L.): a word of caution.  Authors:  T. Todd Jones, Mervin D. Hastings, Brian L. Bostrom, Russel D. Andrews, and David R. Jones  Date Accepted:  Accepted pending minor revisions  Journal:  Journal of Experimental Biology  Comments:  This study was conducted by TTJ under supervision of DRJ. MDH and BLB provided valuable technical assistance. RDA provided expert advice.  xiii  Chapter Three:  Growth of captive leatherback turtles Dermochelys coriacea with inferences on growth in the wild: implication for fisheries induced population decline  Authors:  T. Todd Jones, Mervin D. Hastings, Brian L. Bostrom, Daniel Pauly and David R. Jones  Date Accepted:  In review  Journal: Comments:  This study was conducted by TTJ under supervision of DRJ. MDH and BLB provided valuable technical assistance. DP provided expert advice.  Chapter Four:  Population status and total biomass of Pacific leatherback turtles (Dermochelys coriacea) derived from growth and food conversion studies in captive leatherbacks.  Authors:  T. Todd Jones, Brian L. Bostrom, Mervin D. Hastings, Daniel Pauly and David R. Jones  Date Accepted:  To be submitted May 2009  Journal: Comments:  This study was conducted by TTJ under supervision of DRJ. MDH and BLB and provided valuable technical assistance. DP provided expert advice.  xiv  CHAPTER ONE: GENERAL INTRODUCTION OPENING BY ISHMAEL  Out of the trunk, the branches grow; out of them, the twigs. So, in productive subjects, grow the chapters. ~ Ishmael (Moby Dick)  INTRODUCTION The goal of the research reported in this thesis is to determine the energetics of leatherback turtles (Dermochelys coriacea) in the context of providing a management tool that may help to shape our views on the nature of the global decline in population numbers. Leatherbacks are listed as critically endangered and at risk of extinction in the Pacific Ocean. Data on bioenergetics is perhaps the most useful value physiologists can determine to aid in the conservation of an endangered species. Leatherbacks are extraordinary reptiles with foraging and breeding areas from sub-polar to tropical waters, they are one of the deepest diving air-breathing vertebrates, and are one of a few animals feeding exclusively on gelatinous zooplankton. All of these unique physiological traits must place considerable constraints on their energetics.  Phylogenetic history The first animals recognized as turtles appeared in the paleontological record some 235 million years ago (Gaffney 1990, Pritchard 1997). Two families (Pleurosternidae and Thalassemyidae; 1  now both extinct) invaded the marine world during the Jurassic period, evolving from terrestrial relatives. One-hundred and fifty million years ago in the Cretaceous period four more families of turtle (Toxochelyidae, Protostegidae, Cheloniidae, and Dermochelyidae) entered the sea, of which two are extant, the Dermochelyidae (100 mya) and the Cheloniidae (40 – 60 mya; Pritchard 1997). The only remaining member of Dermochelyidae is the leatherback turtle, Dermochelys coriacea.  Life history All 7 species of extant marine turtles are tied to the land for oviposition and thus share similar life history stages. Males and females occur offshore of nesting beaches (tropical to southern temperate) during breeding seasons. Females lay up to 7 clutches of eggs at 10 to 14 day intervals (Miller 1997) and the breeding season usually lasts 2 to 5 months. Both the males and females then return to tropical to sub-polar foraging areas where the females wait from 2 to 8 years before making another breeding migration, whereas males may make the breeding migration annually (Miller 1997). The only exceptions to this breeding regime are the olive and Kemp‘s ridley marine turtles. Both of these species are known to have mass nesting ―arribadas‖ where thousands of gravid females will nest over a few days (Miller 1997).  Parental care is over once the female leaves the clutch of eggs. The eggs incubate in the sand for 8 to 12 weeks before the hatchlings pip from the eggshell, emerge in unison at night, and scurry down the beach into the water (Miller 1997). Once the hatchlings emerge from the nest they enter a ‗frenzy period‘ consisting of crawling down the beach dunes into the sea and then swiming continuously for up to 24 hours. The only remaining parental investment is the residual  2  yolk sac which will fuel movements for the first 5-11 days before the hatchlings begin feeding (Jones et al. 2007).  Hatchlings of most species of marine turtle swim to major offshore currents where they drift for up to a decade while feeding on organisms within large seaweed drifts (Bolten 1995, Carr 1986). After this initial life stage the juveniles migrate to coastal habitats where they grow to maturity (Carr 1962), while the leatherback alone continues on its oceanic migrations. Flatbacks are perhaps a complete exception remaining in the coastal waters of Australia throughout their life (Bolten 2003).  Marine turtles are found throughout the world‘s oceans and seas (Pritchard 1997). They can be found in the oceanic-pelagic realm, neritic-benthic coastal areas, and in estuarine and intracoastal waterways. Since the grandfather of sea turtle biology ‗Archie Carr‘ began his landmark studies on marine turtles in the 1950s the scope of marine turtle research has grown astronomically. Despite the growth in our knowledge of marine turtle biology six of the seven species are now listed on the IUCN redlist of threatened species with three species on the critically endangered list (hawksbill, Kemp‘s ridley, and leatherback) (IUCN 2008). The leatherback turtle is perhaps in the most danger, with population declines indicating extinction in the Pacific Ocean within a decade (Spotila et al. 2000). But what if anything can the field of physiology offer to conservation efforts?  3  Bioenergetics of Leatherbacks: a crucial contribution to survival Physiology, environment, and resource limitation are the dominant influences on an organism's bioenergetics and thus its life history (Dunham et al. 1989). Resource availability, acquisition and assimilation affect both time and energy allocation to different functions (Congdon et al. 1982, Congdon 1989). Depending on resource availability, metabolic requirements influence reproductive outputs and schedules, as well as growth and morphometrics, which in turn affect overall population demography (Dunham et al. 1989, Brown et al. 2004). Energy requirements at the level of the individual establish the distribution and abundance of the population. Just as growth rates at the individual level set age structure and population demographics. Consequently, metabolic rate (MR) and growth may be the two most important measures to aid in conservation efforts of the critically endangered leatherback turtle (Jones et al. 2004) and are critical for answering fundamental questions about their ecology and life history.  The majority of marine turtle metabolism studies have employed respirometry to obtain MR measurements (see Wallace and Jones 2008 for review), limiting researchers to laboratory measurements of specific activities or measurements of nesting turtles. Specifically for leatherbacks, MR data has been limited to oxygen consumption data on hatchlings (Lutcavage and Lutz 1986, Wyneken 1997, Jones et al. 2007) and nesting females on the beach (Paladino et al. 1990, Lutcavage et al. 1992, Paladino et al. 1996). Of greater interest is to determine the metabolic rate of free ranging animals (Jones et al. 2004).  The doubly labeled water (DLW) method has proven to be a useful tool for studying field energetics of marine animals (Costa 1988). Briefly, the DLW method estimates CO2 production (rCO2) from the divergence between washout curves (i.e., the elimination rates) of heavy 4  hydrogen (deuterium or tritium) and oxygen (18-oxygen) stable isotopes introduced into an animal's total body water (TBW) (Lifson et al. 1955). Specifically, the divergence in isotopic washout curves occurs because hydrogen isotopes are lost via various routes of water turnover (e.g., respiration, defecation, urination, etc.), whereas oxygen isotopes are lost via water turnover but also via rCO2. The resulting difference in the two washout curves approximates total rCO2 by the organism (see Lifson et al. 1955, Speakman 1997 for reviews). Thus, results of DLW experiments provide valuable information about water turnover as well as energy expenditure of animals associated with natural activities during the study period. However, it is important to note that DLW-derived field metabolic rates (FMR) are integrations of energy expenditures during the entire study period, not for particular activities.  Despite the potential utility of DLW, there are considerable disadvantages of the method that generally preclude its use to obtain FMRs for a substantial sample size of large animals. First, the high cost of the isotopes is often prohibitive by itself. Second, the method relies on significant divergence of the isotope washout curves that is created by a relatively higher rCO2 than water turnover rate (rH2O). The accuracy of the DLW method decreases as the ratio of rCO2 to rH2O decreases (Speakman 1997, Butler et al. 2004). This issue typically is not problematic for endothermic animals (Speakman 1997), but can represent risk of failure of the method for ectotherms, particularly those with high rH2O, such as marine turtles.  The water turnover rates in marine turtles are remarkable in comparison with other reptiles. Freeranging lizards and box turtles living in arid to damp environments have water turnover rates of 0.36 to 1.1 ml kg-1 hr-1 (Mautz and Nagy 2000, Penick et al. 2002). Marine turtle water turnover  5  rates range from 2.87 to 5.12 ml kg-1 hr-1 in greens and Kemp‘s ridleys (Ortiz et al. 2000, Southwood et al. 2006) and up to 14.6 ml kg-1 hr-1 in leatherbacks (data adapted from Wallace et al. 2005). These rates are higher than those observed in other reptiles including other marine reptiles such as the marine iguana with a water flux rate of 1.66 ml kg-1 hr-1 (Drent et al. 1999).  Despite these formidable technical challenges, several studies have been published using the DLW method in marine turtles (Wallace et al. 2005, Trullas et al. 2006, Southwood et al. 2006). Considering the numerous potential sources of error inherent to the DLW method, relating DLW-derived MR measurements to simultaneous MR measurements obtained by respirometry is crucial to interpretation of the data acquired via DLW (Speakman 1997). In general, DLW validation experiments indicate that although individual variation might account for serious discrepancies between DLW measurements and those acquired by reference methods, the DLW method tends to overestimate rCO2 by less than 5% among different animal taxa (Butler et al. 2004).  In addition to the DLW method other methods exist for determining FMR such as deploying data loggers to measure heart rates or acceleration of the body and linking these to metabolism (Butler et al. 2004, Wilson et al. 2006). More direct approaches have been used. For instance, Hays et al. (2002) recorded body mass loss of nesting female green turtles to estimate energy spent during fasting periods associated with reproductive cycles. Computational methods based on growth and food conversion (Ivlev 1961, 1966) can be used as well and while these methods are rampant in fishery research (Ricker 1975) they have generally been ignored for other taxa. Furthermore, data from feeding experiments in conjunction with growth and mortality estimates  6  can be used to estimate MR and food consumption throughout the development of an individual as well as consumption rates of populations (Pauly 1986, Palomares and Pauly 1989, Pauly et al. 1993). Therefore, the computational method is more holistic than measures of FMR in individual adult turtles, as estimates of resource requirements can be made for any life-history stage of the animal.  The computational method requires detailed data on growth, in particularly growth functions such as the von Bertalanffy (VBGF), Gompertz, or logistic (von Bertalanffy 1938, Ricklefs 1967). There are several assumptions that must be addressed, however, when using growth functions. The VBGF requires growth to be monotonic throughout postnatal development as the VBGF is a non-inflexion asymptotic size-at-age function (Chaloupka and Musick 1997). Therefore, polyphasic or growth with initial lag phases will be more properly fit into Gompertz or logistic growth functions (Chaloupka and Musick 1997). Most hard shelled turtles experience marked habitat shifts with ontogeny with concomitant changes in diet (e.g., Bjorndal and Bolten 1988). Such transitions more than likely are also reflected in different growth patterns during those individual life stages. Therefore, somatic growth is probably polyphasic, breaking the assumptions of the VBGF (monotonic decay, non-inflexion) but reflective of Gompertz and logistic growth functions. Leatherbacks, however, are epi-pelagic animals throughout their lifehistory (Bolten 2003, Godley et al. 2008) and have no known diet shifts after hatching; their diet consists solely of gelatinous zooplankton (Bjorndal 1997, Salmon et al. 2004). Thus, they probably exhibit monotonic growth.  7  Studies on captive leatherbacks have one glaring problem, however, leatherbacks are notoriously difficult to maintain in captivity (Berkenmeier 1971, Jones et al. 2000) and complications arising from their oceanic-pelagic nature must be addressed, e.g. leatherbacks do not recognize barriers (swim continuously into tank sides), are obligate gelativores (feed solely on gelatinous zooplankton), and are highly susceptible to bacterial and fungal infections. The majority of leatherbacks raised in captivity have not lived past 100 days (Deraniyagala 1939, Frayr 1970, Spozynska 1970, Berkenmeier 1971, Witham 1977, Bels et al. 1988, Jones et al. 2000). While MR data are valuable by themselves, applications of MRs to broader, multi-faceted questions increase the relevance and importance of MR data to the study of marine turtle ecology and conservation. Metabolic rates, particularly FMRs, for marine turtles are the most critical components in calculating individual and population energy requirements, and the total amount of energy required by the population ultimately defines population structure and sustainability (Jones et al. 2004).  Thesis organization I have organized my thesis into three data chapters in manuscript form with a final summary chapter. In this thesis I used laboratory experiments, computational modeling, and compiled fisheries bycatch data to provide information on the growth and metabolic rate of leatherback turtles in context of population decline. Chapter two presents the results from validating the use of doubly labeled water and water turnover rates in marine turtles and discusses the practicality of its use. Chapter three presents the results of growth rates and age-at-maturity estimates in the context of fisheries interactions (bycatch). Chapter four uses computational modeling with growth, feeding experiments, and mortality estimates to determine individual as well as  8  population level energy requirements. And the final chapter (five) summarizes the major results and gives future directions for research. Finally it should be noted that this dissertation has been written in manuscript style therefore chapters 2-3 are stand alone papers that have been accepted for publication or are in review. This leads to a fare amount of repetition throughout the dissertation.  Objectives  Chapter two: Validation of the Use of Doubly Labeled Water for Estimating Metabolic Rate in the Green Turtle (Chelonia mydas L.): a word of caution    Determine the validity of the use of doubly labeled water (DLW) to measure field metabolic rate (FMR) in marine turtles    Assess if DLW is constrained by the physiological state of the organism, i.e. feeding and fasting    Determine the association of feeding and fasting with water turnover and metabolic rate  Chapter three: Growth of captive leatherback turtles Dermochelys coriacea with inferences on growth in the wild: implication for fisheries induced population decline    Develop protocols to maintain leatherbacks in captivity    Determine growth rates and age-at-maturity estimates    Summarize bycatch data for the Pacific Ocean and determine at what length (straight carapace length) leatherbacks begin to interact with commercial fisheries  9  Chapter four: Population status and total biomass of Pacific leatherback turtles (Dermochelys coriacea) derived from growth and food conversion studies in captive leatherbacks    Determine the ontogeny of food consumption through feeding experiments, food conversion, and growth in captive leatherbacks    Relate food consumption to daily energy requirements    Determine food (and energy) consumption rates and biomass of leatherback populations using mortality estimates and the derived food consumption and growth data  10  REFERENCES Bels V, Rimblot-Baly F, Lescure J (1988) Croissance et maintien en captivité de la tortue luth Dermochelys coriacea (Vandelli, 1761). Revue Francaise d'Aquariologie 15(2): 59-64 Birkenmeier E (1971) Juvenile leatherback turtles, Dermochelys coriacea (Linnaeus), in captivity. Brunei Museum Journal 3(1): 160-172 Bjorndal KA (1997) Foraging Ecology and Nutrition of Sea Turtles. In: Lutz P and Musick J (eds) The Biology of Sea Turtles. CRC Press, Boca Raton. pp 199-231 Bjorndal KA, Bolten AB (1988) Growth rates of immature green turtles, Chelonia mydas, on feeding grounds in the southern bahamas. Copeia 3: 555-564 Bolten AB (1995) Biology of the early pelagic stage the ‗lost year‘. Biology and Conservation of Sea Turtles (Revised Edition). Smithsonian Institution Press. Washington, D.C. pp 579 – 581 Bolten AB (2003) Variation in sea turtle life history patterns: neritic vs. oceanic development stages. In: Lutz PL, Musick JA and Wyneken J (eds) The Biology of Sea Turtles. CRC Press, Boca Raton 2: 243-258 Brown JH, Gillooly JF, Allen AP, Savage VM, West GB (2004) Toward a metabolic theory of ecology. Ecology 85: 1771–1789 Butler PJ, Milsom WK, Woakes AJ (1984) Respiratory, cardiovascular and metabolic adjustments during steady state swimming in the green turtle, Chelonia mydas. Journal Comparative Physiology B 154: 167-174 Carr A (1962) The ecology and migrations of sea turtles. 6. Comparative features of isolated green turtle colonies. American Museum Natatives. 2091: 1-42 Carr A (1986) Rips, fads and little loggerheads. Bioscience 36(2): 92 – 110  11  Chaloupka MY, Musick JA (1997) Age, growth, and population dynamics. In: Lutz P, Musick J (eds) The Biology of Sea Turtles. CRC Press, Boca Raton. pp. 233-276 Congdon JD (1989) Proximate and evolutionary constraints on energy relations of reptiles. Physiological Zoology 62: 356–373 Congdon JD, Dunham AE, Tinkle DW (1982) Energy budgets and life histories of reptiles. In: Gans C, Pough FH (Eds.), Biology of the Reptilia, vol. 13. Academic Press, New York, pp. 233–271 Costa DP (1988) Methods for studying the energetics of freely diving animals. Cananadian Journal of Zoology 66: 45–52 Deraniyagala PEP (1939) The Tetrapod Reptiles of Ceylon Volume I. Testudinates and Crocodilians. Ceylon Journal of Science. Sunil Printers, New Delhi Drent J, van Marken Lichtenbelt WD, Wikelski M (1999) Effects of Foraging Mode on the Energetics of the Marine Iguana, Amblyrhyncus cristalus. Functional Ecology 13: 493 – 499 Dunham AE, Grant BW, Overall KL (1989) Interfaces between biophysical and physiological ecology and the population ecology of terrestrial verterbrate ectotherms. Physiological Zoology 62: 335–355 Frayr W (1970) The world's largest living turtle. Salt Water Aquarium 5: 235-241 Gaffney ES (1990) The comparative osteology of the Triassic turtle Proganochelys. Bulletin American Museum of Natural History 194: 1 – 263 Godley BJ, Blumenthal JM, Broderick AC, Coyne MS, Godfrey MH, Hawkes LA, Witt MJ (2008) Satellite tracking of sea turtles: Where have we been and where do we go next? Endangered Species Research 4: 3-22  12  Hays GC, Broderick AC, Glen F, Godley BJ (2002) Change in body mass associated with longterm fasting in a marine reptile: the case of green turtles (Chelonia mydas) at Ascension Island. Canadian Journal of Zoology 80: 1299–1302 IUCN 2008. 2008 IUCN Red List of Threatened Species. <www.iucnredlist.org>. Downloaded on 20 February 2009. Ivlev VS (1961) Experimental ecology of the feeding of fishes. Douglas S (translation). Yale University Press, Massachusetts pp 302 Ivlev VS (1966) The biological productivity of waters. Ricker WE (translation). Journal of Fishery Research Board of Canada 23(11): 1727-1759 Jones TT, Salmon M, Wyneken J, Johnson C (2000) Rearing leatherback hatchlings: protocols, growth and survival. Marine Turtle Newsletter 2000 (90): 3-6 Jones DR, Southwood AL, Andrews RD (2004) Energetics of Leatherback Sea Turtles. In (Eds. Gordon M. S. and S. M. Bartol) Experimental Approaches to Conservation Biology. University of California Press, Berkeley. pp 66 – 82 Jones TT, Reina RD, Darveau C-A, Lutz PL (2007) Ontogeny of energetics in leatherback (Dermochelys coriacea) and olive ridley (Lepidochelys olivacea) sea turtle hatchlings. Comparative Biochemistry Physiology Part A 147: 313-322 Lifson N, Gordon GB, McClintock R (1955) Measurement of total carbon dioxide production by means of D2O18. Journal of Applied Physiology 7: 704–710 Lutcavage ME, Lutz PL (1986) Metabolic rate and food energy requirements of the leatherback sea turtle, Dermochelys coriacea. Copeia 1986(3): 796–798 Lutcavage ME, Bushnell PG, Jones DR (1992) Oxygen stores and aerobic metabolism in the leatherback sea turtle. Canadian Journal of Zoology 70: 348-351  13  Mautz WJ, Nagy KA (2000) Xantusiid lizards have Low Energy, Water, and Food Requirements. Physiological and Biochemical Zoology 73(4): 480 – 487 Miller JD (1997) Reproduction in sea turtles. In (Lutz P. and J. Musick eds.) The Biology of Sea Turtles. CRC Press, Boca Raton 51 – 81 Ortiz RM, Patterson RM, Wade CE, Byers FM (2000) Effects of Acute Fresh Water Exposure on Water Flux Rates and Osmotic Responses in Kemp‘s Ridley Sea Turtles (Lepidochelys kempi). Comparative Biochemicstry and Physiology Part A 127: 81 – 87 Paladino FV, O'Connor MP, Spotila JR (1990) Metabolism of Leatherback Turtles, Gigantothermy,and Thermoregulation of Dinosaurs. Nature 344: 858 Paladino FV, Spotila JR, O‘Connor MP, Gatten Jr. RE (1996). Respiratory Physiology of Adult leatherback Turtles (Dermochelys coriacea) While Nesting on Land. Chelonian Conservation and Biology. 2(2): 223 – 229 Palomares ML, Pauly DP (1989) A multiple regression model for predicting the food consumption of marine fish populations. Australian Journal of Marine and Freshwater Research 40: 259-73 Pauly D (1986) A simple method for estimating the food consumption of fish populations from growth data and food conversion experiments. Fishery Bulletin 84(4): 827-842 Pauly D, Sambilay V JR, Opitz S (1993) Estimates of relative food consumption by fish and invertebrate populations, required for modeling the Bolinao reef ecosystem, Philippines. In Christensen V, Pauly D (eds) Trophic models of aquatic ecosystems. ICLARM Conference Proceedings 26: 236-251  14  Penick DN, Congdon J, Spotila JR, Williams JB (2002) Microclimates and Energetics of FreeLiving Box Turtles, Terrapene carolina, in South Carolina. Physiological and Biochemical Zoology 75: 57 – 65 Pritchard PCH (1997) Evolution, phylogeny, and current status. In: Lutz P, Musick J (eds) The Biology of Sea Turtles. CRC Press, Boca Raton 1 – 28 Ricker WE (1975) Computational and interpretation of biological statistics of fish populations. Bulletin 191 Department of the Environment Fisheries and Marine Service, Ottawa Ricklefs RE (1967) A graphical method of fitting equations to growth curves. Ecology 48(6): 978-983 Salmon M, Jones TT, Horch K (2004) Ontogeny of diving and feeding behavior in juvenile sea turtles: A comparison study of green turtles (Chelonia mydas L.) and leatherbacks (Dermochelys coriacea L.) in the Florida current. Journal of Herpetology 38: 36-43 Speakman JR (1997) Doubly Labelled Water: Theory and Practice. London: Chapman & Hall. pp. 399 Spotila JR, Reina RD, Steyermark AC, Paladino FV (2000) Pacific leatherback turtles face extinction. Nature 405: 529-530 Spoczynska JOI (1970) Rearing hatchlings of Dermochelys coriacea L. British Journal of Herpetology 4: 189-192 Southwood AL, Reina RD, Jones VS, Speakman JR, Jones DR (2006) Seasonal metabolism of juvenile green turtles (Chelonia mydas) at Heron Island, Australia. Canadian Journal of Zoology 84: 125–135  15  Trullas S, Spotila JR, Paladino FV (2006) Energetics during hatchling dispersal of the olive ridley turtle Lepidochelys olivacea using doubly labeled water. Physiological Biochemical Zoology 79: 389–399 von Bertalanffy L (1938) A quantitative theory of organic growth (Inquiries on growth laws. II.). Human Biology 10(2): 181-213 Wallace BP, Williams CL, Paladino FV, Morreale SJ, Lindstrom RT, Spotila JR (2005) Bioenergetics and diving activity of interesting leatherback turtles Dermochelys coriacea at Parque Nacional Marino Las Baulas, Costa Rica. Journal of Experimental Biology 208: 3873–3884 Wallace BP and Jones TT (2008) What makes marine turtles go: A review of metabolic rates and their consequences. Journal Experimental Marine Biology and Ecology 356: 8-24 Wilson RP, White CR, Quintant F, Halsey LG, Liebsch N, Martin GR, Butler PJ (2006) Moving towards acceleration for estimates of activity specific metabolic rate in free-living animals: the case of the cormorant. Journal of Animal Ecology 75: 1081–1090 Witham R (1977) Dermochelys coriacea in captivity. Marine Turtle Newsletter 3: 6 Wyneken J (1997) Sea turtle locomotion: mechanisms, behavior, and energetics. In: Lutz PL, Musick JA (Eds.) The Biology of Sea Turtles, vol. 1. CRC Press, Boca Raton, FL, pp. 165– 198  16  CHAPTER TWO: VALIDATION OF THE USE OF DOUBLY LABELED WATER FOR ESTIMATING METABOLIC RATE IN THE GREEN TURTLE (CHELONIA MYDAS L.): A WORD OF CAUTION1 INTRODUCTION Daily energetic expenditure and time energy budgets are useful for gaining insight into an animal‘s daily food requirements and allocation of energy to various activities (i.e. growth, reproduction, foraging, movement). Construction of time-energy budgets requires detailed observations of behavior in the wild and replication of observed activities in the laboratory while simultaneously measuring energy expenditure through indirect calorimetry for metabolic rate (MR) determinations (Speakman, 1997). Time-energy budgets have been determined for loggerhead and leatherback turtles (Kraemer and Bennett, 1981; Lutcavage and Lutz, 1986; Davenport, 1998; Jones et al., 2007), but the logistic difficulties of using this approach with marine turtles (i.e., simulating diving, swimming and feeding on natural foods) have led researchers to investigate other options. The use of doubly labeled water (DLW) to study the energetics of free-ranging animals (Lifson and McClintock, 1966) has become increasingly popular in the field of physiological ecology (Speakman, 1997; Nagy et al., 1999), and field metabolic rates (FMR) of a wide variety of taxa, including marine turtles, have been determined using this technique (Nagy et al., 1999, Wallace et al., 2005; Southwood et al., 2006; Trullas et al., 2006).  1  A version of this chapter has been accepted for publication pending minor revisions. Jones TT, Hastings MD, Bostrom BL, Andrews RA, Jones DR. Journal of Experimental Biology. 17  The DLW method requires capturing and dosing a study animal with water that has been enriched with isotopes of hydrogen (2H; Deuterium or 3H, tritium) and oxygen (18O). A blood sample is taken before injection of DLW to determine background enrichment of the isotopes naturally occurring in the animal. A second blood sample is taken when the injected isotopes reach equilibration with the animal‘s body water, thus giving the isotope dilution space (IDS). The IDS is used to infer total body water (TBW). The animal is then released and recaptured for a final blood sample. During the period when the animal is at large, water added to the animal‘s TBW (i.e. water influx) due to drinking and metabolic water production is unlabeled, while the labeled isotopes that have equilibrated with the animal‘s TBW, 2H and 18O, are lost through water efflux due to urination, defecation, evaporation, and tear production (salt gland secretion). 18  O is also lost as CO2 due to cellular respiration and the difference in slopes of 2H and 18O  washout, over time, yields a value for CO2 production. If the animal‘s respiratory quotient (RQ) or measured respiratory exchange ratio (RER) is known, an estimate of CO2 production provided by the DLW method can be used to calculate metabolic rate (however, many reptiles excrete respiratory derived CO2 as bicarbonate from the cloaca [Coulson and Hernandez, 1964] which is not detected in RER measurements, causing errors in MR determinations). Additionally, the washout slope of 2H multiplied by TBW gives an estimate of daily water flux. The procedures and assumptions of the DLW method are described in detail by Nagy (1989) and Speakman (1997).  The use of DLW does not give meaningful results in some animals, and validation of the technique is recommended for studies involving species which have potentially unique  18  physiologies or habitats (Nagy, 1980; Speakman, 1997; Nagy et al., 1999). The effectiveness of the DLW method for use with a given species can be determined by simultaneously measuring metabolic rate using DLW and respirometry or calorimetry. The DLW method has been shown to work in some reptiles (Nagy, 1983; Nagy et al., 1999; Anderson et al., 2003) but these studies were performed on terrestrial reptiles which could be considered water conservers. Field metabolic rates for marine iguanas have been determined with DLW (Nagy and Shoemaker, 1984; Drent et al., 1999), however the iguanas spent substantial time on land and had moderate water turnover rates. The problem associated with using DLW in aquatic animals is that high water turnover rates and subsequent rapid washout of isotopes reduces the difference between the 18O and 2H washout curves to less than the variability in mass spectrometry measurements. In animals with large water turnover rates it is possible that > 90 % of 18O is washed out as water flux with 2H. When > 90 % of labeled oxygen turnover is due to water exchange the difference in isotope washouts does not give an accurate measurement of CO2 production (Speakman, 1997). Despite the risk that the DLW may give inaccurate results for aquatic animals, DLW has been used in metabolic determinations of marine turtles without validation (Wallace et al., 2005; Southwood et al., 2006; Trullas et al., 2006).  We conducted a study to determine the validity of using the DLW method for estimating metabolic rate of green turtles (Chelonia mydas Linnaeus). Turtles were injected with DLW and washout of isotopes was monitored by taking daily blood samples. We simultaneously recorded oxygen consumption using open-flow respirometry; and MRs calculated using DLW and respirometry were compared. Although DLW has been used in marine turtles this study represents the first validation of its use in these turtles.  19  MATERIALS AND METHODS  Animals six green turtles were imported from the Cayman Turtle Farm (1983 Ltd.; Grand Cayman, British West Indies; CITES Export permit 2002/ky/000112) to the Zoology Animal Care Center, Department of Zoology, University of British Columbia (CITES Import permit CA02CWIM0129). Turtles were maintained and research was conducted under Animal Care Protocol A03-0255 from the UBC Animal Care Committee.  Turtles were kept in a large oval fiberglass tank (10m x 3m x 1.5m) filled with seawater (holding tank) except when they were kept in isolation (isolation tanks) for an experiment. Water quality for the pool was maintained by two filter systems (1) a biological/mechanical filter (built by UBC – Zoology Workshop staff) containing a protein skimmer, bio-balls™ and fiberglass mat and (2) two sand filtration systems (TRITON® II TR 100; Pentair Pool Products™, Sanford, North Carolina, USA) designed for large pools. The water temperature was maintained at 24 ± 1 C. Fluorescent light fixtures (40 W UVA/B; Repti-Glow® 8) suspended above each tank provided full spectrum radiation for 12 hours each day; the tanks were also exposed to ambient light. Water quality was maintained between the following levels: pH = 8.0-8.3; salinity = 33-35; and ammonia < 0.1 mg-1. Monthly water changes prevented accumulation of high levels of ammonia, bacteria and fungi.  Turtles were fed a diet of Purina Trout Chow® 5D-VO5 (Purina Mills, LLC, St. Louis, Missouri, USA) mixed with a aqueous solution of flavorless gelatin, Reptavite® and Reptamin® (vitamin and mineral supplements). Dried homogenized samples of the food were analyzed by bomb 20  calorimetry (Parr Instrument Co., Moline, Illinois, USA) at the Southwest Fisheries Science Center of the National Oceanic and Atmospheric Administration (NOAA, La Jolla, California, USA). The diet contained 41% protein, 12% lipids, 4% fiber and had ~ 17,000 kJ kg-1 dry mass (DM). The turtles were fed 1-2% of body mass every other day. Food quantities were based on presumed daily calorific intake of wild green turtles (Bjorndal, 1996).  Experimental design A turtle was removed from the holding tank and a background blood sample was drawn from the cervical venous sinus. A measured dose of DLW (oxygen-18, 18O; deuterium, 2H) was injected into the turtle‘s coelemic cavity (intra-coelomic; IC) (see below). The equilibration time curve was followed over 10 hours after DLW injection (Fig. 2.1). Blood was drawn every hour up to 5 hours to determine time to isotopic equilibration with body water. A final blood sample was drawn at 10 hours (Fig. 2.1) and the turtle was placed in an isolation tank. At 24 hours another blood sample was drawn and this sample served as the initial isotope level for the start of the validation experiment. The turtle was then placed inside another isolation tank equipped with a respirometer. Blood samples were drawn once a day for 5 days while the turtle‘s oxygen consumption rate was measured continuously. Turtles were fed during this period using the normal feeding regime. To prevent possible isotope re-entry from drinking, the tank was flushed with fresh seawater every other day. The turtle was then fasted for 10 days. On day 15 (5 days of fed trial and 10 days of fasting) a blood sample was drawn, the turtle was injected with a 2H boost (IC) and a blood sample was drawn after 5 hours (to determine IDS and initial isotope levels for the fasting trial). A blood sample was drawn on each day of the fasting trial (days 16 to 20) while the turtle‘s oxygen consumption rate was continuously recorded. As in the feeding  21  trial, complete tank flushes were performed every other day to prevent isotope re-entry. After the last blood sample the turtle was given a second 2H boost (IC) and 5 hours later a blood sample was taken (to determine IDS) (Fig. 2.2). The normal feeding regime was then resumed. Blood samples were drawn for 5 days after the end of the fasting trial to determine water turnover rates post-fasting. The time course of a complete experiment is shown in Figs 2.1, 2.2.  There is a possibility that labeled isotopes could be stored in the body through anabolic pathways during the feeding trial (when the animal is in energetic equilibrium) and then released during the fasting trial (J. Speakman, personal communication) causing error in the fasting DLW measurements. If 18O were released this would cause enrichment of isotope in the turtle and thus mask loss of 18O through respiration, indicating photosynthesis! Therefore, we performed a separate fasting trial with 2 turtles. The turtles were fasted for 10 days before a blood sample was taken to determine background isotopic levels. A measured dose of DLW was injected and an equilibration sample was taken 5 hours later. At 24 hours a blood sample was drawn to determine the initial isotope level for the start of fasting trial. Blood samples were drawn for the next 3 days while the turtle‘s oxygen consumption was measured. On the 3rd day of the trial a final blood sample was taken and then the turtle was given a 2H boost, 5 hours later another blood sample was drawn to obtain the equilibration sample for the 2H boost.  Respirometry The isolation tank (1.5m x 1m x 1.5m), filled with sea water, was covered by an acrylic respirometry dome to trap expired gases. The salinity and temperature of the seawater were 34.7 ± 0.4 and 25.8 ± 0.7 C for fed turtles, and 34.9 ± 0.4 and 25.1 ± 1.0 C for fasted turtles,  22  respectively. Turtles were able to move freely inside the tank. Turtles were trained to breath into the respirometry dome, which had an air space of ~ 10 L. Air of known partial pressures of O2 and N2 (CO2 and water vapor free) flowed through a Sierra Side-Trak 840 Mass Flow Controller (MFC) (Sierra Instruments, Monterey, California, USA) into the dome. The MFC regulated flow to 8 L min-1. Ex-current air was sub-sampled at 250 ml min-1 and scrubbed of water vapor (Drierite® water absorbent, W. A. Hammond DRIERITE Co. LTD, Xenia, OH, USA) before being drawn through an Applied Electrochemistry O2 Analyzer S-3A (AEI Technologies, Pittsburg, Pennsylvania, USA). Data from the MFC and O2 analyzer were recorded at a frequency of 1 Hz and later analyzed using Sable Systems® DataCan V Data Acquisition & Analysis Software & Hardware (Sable Systems International, Las Vegas, NV, USA). The openflow respirometer was calibrated using the Nitrogen dilution technique (Fedak et al., 1981). Oxygen consumption data was corrected to STPD.  RER (i.e. CO2 production/O2 consumption) was measured using the recording system described above but the turtles were kept in an acrylic dry box. The box was 1m x 0.5m x 0.5m, with a clamp down lid, and a thin rubber section on one side which acted as a pressure damper. Turtles were placed in the respirometer and both O2 consumption and CO2 production were measured, the latter using an Applied Electrochemistry CO2 Analyzer CD-3A. Measurements were made over 1.5 hours or more, sufficient time for gases to equilibrate and to establish stable gas exchange values from the animal. RER trials were done in the fed state and after 10 and 15 days of fasting. The dry box respirometer was calibrated using the nitrogen dilution technique.  23  Doubly labeled water determinations Turtles were weighed on an ADAM CPW-60 0-60 kg ± 0.02 kg digital scale (Dynamic Scales, Terre Haute, IN). For blood sampling purposes turtles were placed head down on a bench with a 45 declination (to aid venous pooling of the blood) and held with straps. All blood samples (2-5 ml) were drawn from the cervical venous sinus using 21 gauge x 1.5 inch BD needles and BD SST Gel and Clot Activator Vacutainers® (BD; Becton, Dickinson and Co., Franklin Lakes, NJ, USA). All blood samples were left to clot for 30 minutes before centrifuging for 30 min at 3000 rpm. Serum was removed and transferred to NalgeneTM cryo-safe plastic tubes and frozen. For DLW injections a 21 gauge x 3.5 inch needle (BD) was used to penetrate the body cavity (coelemic cavity) just anterior to the rear flipper and angled 45 towards the midline (Southwood et al., 2006). All blood and injectate (18O & 2H) samples were later analyzed for 2H and 18O isotope concentrations by Metabolic Solutions, Inc. (Nashua, NH, USA).  DLW dose administered to each turtle was based on the following equation (Speakman, 1997):  Dosage (ml) = [(TBW x body mass in grams) x desired intitial enrichment]/injectate enrichment.  TBW was assumed to be 66% of body mass (Thorson, 1968) in the initial calculation. TBW of our animals was confirmed when we analyzed data from the first turtles. Desired initial enrichment (DIE) was determined from published curves for mammals (Speakman, 1997) and preliminary green turtle washout estimates and Southwood et al. (2006) with the goal of having enrichment levels at least 150 p.p.m. above background levels at the end of 20 days. Injectate enrichment from the mixture high-enrichment 2H (99.9 atom%; Isotec, Inc., Miamisburg, OH, 45342 USA) and 18O (95.1 atom%; Isochem UK Ltd., Banstead, Surrey, SM7 2LJ United 24  Kingdom), and the dose given are shown in Table 2.1. The actual dose administered to a turtle was determined by weighing the injectate syringe before and after drawing the mixed DLW into the syringe (Sartorius BP2105 digital scale ± 0.0001g Goettingen, Germany). A 3-way stopcock and a separate syringe filled with 0.9% NaCl solution (2 times the volume of the DLW dose) was used to flush out the injectate syringe into the turtle‘s body cavity. The total injection (dose + flush) was less than 0.1% of TBW for all turtles.  Analysis of isotopic data The turnover (washout) rates for 2H and 18O (kd and ko, respectively) were determined using the two-sample technique (Speakman, 1997) measuring isotope decay over the time period from the first and last isotope determination (day 1 and day 6, fed DLW trials; day 16 and day 21, fasted DLW trials). The two-sample approach was used and reported in Table 2.1 as this is the common, and typically only, method available to researchers working in the field. For comparative purposes and to use all available data we also used the multiple-sample approach (MSA; Speakman, 1997) where kd and ko are determined from a curve fitted to the loge transformed daily isotope determinations (after subtracting background levels to obtain excess isotope levels) (Fig. 2.3).  The plateau method was used to determine isotope dilution space and the 2H dilution space (Nd) was used to infer TBW. Typically the 18O dilution space is used as it has been shown to be closer to real TBW values in dessication studies (Speakman, 1997), however, as we re-boosted the turtles with 2H (cheaper than 18O) to determine IDS, to obtain TBW before and after the fasting trial, we used the 2H dilution space throughout for consistency. There is typically a 4%  25  underestimate of TBW when it is calculated from 18O rather than 2H (Speakman 1997). Water turnover rates were determined by multiplying Nd by kd (Speakman, 1997). Body water pools for fed turtles were determined by measuring the IDS pre-trial and averaging this with the IDS posttrial. The post-trial IDS was calculated as percentage of the mass of turtle at the end of the experiment. The 2H dilution space measured pre- and post-trials showed that body water pools were stable. CO2 production was determined using several equations which allowed us to determine a best practice for DLW studies of marine turtles. The equations, only 3 of which have been used in marine turtle studies, were as follows (where rCO2 = CO2 production):  One-pool method by Lifson and McClintock (1966), used by Trullas et al. (2006) in emergent, hatchling olive ridleys,  eq. 1) rCO2=(N/2.08)(ko-kd)-(0.015.kd.N), where N=No  One-pool method by Speakman (1997),  eq. 2) rCO2=(N/2.078)(ko-kd)-(0.0062.kd.N), where N=No  A two-pool method by Coward et al. (1985),  eq. 3) rCO2=(1/2.08)(No.ko-Nd..kd)-(0.015.Nd.kd)  Two-pool method by Schoeller (1988), used by Southwood et al. (2006) for green turtles,  26  eq. 4) rCO2=(N/2.078)(1.01ko-1.04kd)-(0.0246.N.1.05)(1.01.ko-1.04.kd), where N=[(No/1.01)+(Nd/1.04)]/2  Two-pool method by Speakman et al. (1993),  eq. 5) rCO2=(N/2.078)(1.01ko-1.0532kd)-(0.0246N1.05)(1.01ko-1.0532kd), where N=[(No/1.01)+(Nd/1.0532)]/2  Two-pool method by Speakman (1993), used by Wallace et al. (2005) for internesting leatherbacks,  eq. 6) rCO2=(N/2.078)(ko-Rdilspace.kd)-(0.0246N1.05)(ko-Rdilspace.kd), where N=[(No+Nd/Rdilspace)]/2 and Rdilspace=Nd/No  Two-pool method by Speakman (1997),  eq. 7) rCO2=(N/2.078)(ko-Rdilspacekd)-(0.006N.Rdilspace.kd), for N see eq. 6 above  CO2 production was converted to energy expenditure using the measured RER for fed and fasted turtles (fasted measurements made at both 10 and 15 days of fasting). All values are listed in Table 1 thus our data may be re-analyzed by other researchers for comparative purposes or revised as the techniques and equations advance and change (Speakman, 1997). Statistical  27  comparisons between two treatment groups were done with Student‘s t-test. For more than 2 treatment groups a one-way ANOVA was used to determine if significant differences existed between treatment groups and a Tukey-Kramer post-hoc test to determine where significant differences lay. A Welch ANOVA, testing equality of means when standard deviations are not equal (correcting for unequal variances), was used for comparisons between MRs obtained from respirometry and DLW. In all statistical analyses alpha was set to 0.05. All statistical analyses were done on JMP® 4 statistical software program (SAS Institute INC., 2001). All values are given as means ± 1 s.d.  RESULTS The results from the isotopic as well as the respirometric analyses are summarized in Tables 2.1 and 2.2. All data for individual turtles including IDS, kd & ko, injectate quantity and enrichment, and RER are given in order to keep the validation transparent. There was a trend for body mass to drop with fasting and increase post-fasting, however any changes from initial values were < 2% and not significant (f ratio=0.059, p=0.9929). Turtles L2 and R4 were not used in the calculation of MR (DLW or respirometry) for the feeding trials as they had kd:ko ratios of 0.95 and 0.96, respectively (Table 2.1), thus they were outside the range of acceptable kd:ko values (Speakman 1997).  28  Respirometry Metabolic rate dropped 51.95% from 28.66 ± 5.31 to 13.77 ± 1.49 kJ kg-1 day-1 during fasting (ttest=-6.273, p<0.0001). Interestingly, while there was a significant drop in MR the turtles showed no change in breathing frequency (0.10 ± 0.02 and 0.09 ± 0.03 breaths min-1, for fed and fasted, respectively; t-test=0.838, p=0.4217). RER showed a significant drop from fed (0.83 ± 0.07) to fasted states at 10-days (0.53 ± 0.18) and 15-days (0.59 ± 0.09) (f ratio=8.2255, p=0.0039). There was no significant change in RER during fasting from 10 to 15 days.  Doubly labeled water determinations Using the plateau method the 2H and 18O dilution spaces (Nd & No) were 15.7 ± 1.6 and 15.0 ± 1.5 L, respectively. The dilution space ratio (Nd:No) was 1.048 ± 0.005 which is within the range typically found across taxa (Speakman, 1997). The kd:ko ratio was 0.91 ± 0.02 for fed turtles (excluding L2 & R4) and increased significantly to 1.08 ± 0.16 for fasted turtles (t=2.338, p=0.0415). Estimating TBW from the 2H dilution space gave values of 69.64 ± 3.98 % of body mass which decreased significantly to 61.14 ± 7.95 and 60.77 ± 5.09 % of body mass after 10 and 15 days fasting, respectively (f ratio=4.3517, p=0.0323). Water turnover dropped 45.33% from the fed to fasted state and returned to pre-fasting levels within 1 day of post-fasting trials. All these changes were significant (f ratio=27.9044, p<0.0001). Similar differences were seen in water flux as % TBW day-1, however, the drop from fed to fasted was 35.84% and at the end of the fasting trial the turtles showed a compensatory rebound; water flux increased 18.97% above pre-fasting levels returning to normal feeding levels by 5 days post-fasting (f ratio=19.0584, p<0.0001).  29  The seven equations compared in the analysis are listed in Table 2.2. The two-sample technique gave MRs ranging from 16.40 ± 19.83 to 66.91 ± 21.56 kJ kg-1 day-1, a four-fold difference, depending on equation used. Equation 7, the two-pool method of Speakman (1997), gave the closest MR to that obtained through respirometry (mean values 30.85 ± 21.01 and 28.66 ± 5.31 kJ kg-1 day-1, respectively) a mean absolute difference of only 7.67%, the other equations differed from respirometry by 34 to 133%. The difference between MR derived from eq. 7 and respirometry was not significantly different (using a Welch ANOVA for unequal variances, ttest=0.0601, p=0.952). Furthermore, when the multiple-sample approach was used for determining kd and ko, eq. 7 gave MR of 29.86 ± 20.72 kJ kg-1 day-1 which was only 4.19% above the respirometry value. These percent differences were from comparing group means whereas the average of the % differences between individuals for eq. 7 and respirometry are 53.24 ± 20.14 and 54.70 ± 15.80% for the two-sample technique and MSA, respectively.  All of the above DLW energetic determinations and comparisons are for fed-trials only. DLW water comparisons cannot be made for the fasting trials as negative MRs were obtained (i.e., 46.55 ± 47.90 kJ kg-1 day-1, for eq. 7). We conducted a second trial on a subsample (n=2) of turtles, fasting them first and injecting DLW just before the respirometry trials to eliminate the possibility that the isotope could be stored in fed animals and released later during fasting. Again we obtained negative MR determinations, -9.75 ± 5.18 kJ kg-1 day-1 (eq. 7). In all fasting trials the kd was greater than ko.  30  DISCUSSION Application of the DLW method in the present study gave MR estimates that were greater than respirometry by 8% and 4% for the two-sample and multiple-sample approaches, respectively, when using eq. 7 (Speakman, 1997) with fed turtles. The difference between DLW and respirometry MR estimates in both cases was not significant (Table 2.1). The two-sample technique is typically the only approach available to field researchers as recapturing the study animals multiple times can be difficult and disrupts the animal‘s natural behavior, but our data suggests the two-sample technique results in twice the error of the multiple sample approach in marine turtles. Overestimation by ≤ 8% for energy expenditure by the DLW method, when compared with respirometry, is common when group means are used (Speakman 1997; Butler et al. 2004); however, the % difference between DLW and respirometry MR estimates for individuals ranged from -51 to 82%. As stated by Speakman (1997), the DLW technique is extremely limited in its ability to determine the MR of single individuals, or to make comparisons of individuals in different activities or metabolic state, however, the technique is quite adequate to determine group energetic demands.  The different equations listed in Table 2.2 gave MRs that deviated from respirometry by 8 to 133%. The worst estimates were derived from the single-pool equation of Speakman (1997). While it is generally accepted that the two-pool method is more accurate in estimating water turnover in large animals there is uncertainty about lower bound of ―large‖ body mass, especially in reptiles. Thus we calculated MR using all 7 equations (2 single-pool and 5 two-pool) to illustrate equation dependent effects on the calculations of MR (Table 2.2).  31  A major source of error in DLW calculations can lie in the estimate of fractionation. For instance, eqs. 6 & 7 differ in their fractionation correction and MR output differs by nearly 25%. Fractionation is a measure of the degree of discrimination that exists between isotopes when released by various routes from the body. A major problem in reptile studies is that corrections for fractionation are derived mostly from studies of humans and other mammals. For instance, Schoeller et al. (1986) corrected for fractionation assuming a body temperature of 37 C. As marine turtles have lower body temperature (ectothermic), except for perhaps the leatherback, and probably near zero transcutaneous water loss, it is imperative that the equation used in calculation of MR does not over-correct for fractionation based on mammalian studies. Therefore, it is no surprise that eq. 7 gave the best DLW-derived MR (Table 2.2), as this equation reduced the overcorrection of fractionation of Lifson and McClintock (1966) by 50%. The other two-pool equations used in our calculations incorporate the higher fractionation correction originally established by Lifson and McClintock (1966).  None of the three previous marine turtle DLW studies (leatherbacks, Wallace et al., 2005; green turtles, Southwood et al., 2006; olive ridley hatchlings, Trullas et al., 2006) used eq. 7 by Speakman (1997). While the DLW-derived MR for Wallace et al. (2005) and Southwood et al. (2006) are in plausible ranges, the use of eq. 7 could lower Wallace et al.‘s MR by 25% and Southwood et al.‘s MR by nearly 37%. This would reduce Southwood et al.‘s (2006) MRs to 1.8-3.2 times resting which are probably more typical of FMRs and place Wallace et al.‘s FMRs in the middle of the range for RMR and diving metabolic rates in leatherbacks (Wallace and Jones, 2008). Trullas et al. (2006) used the single-pool equation (eq. 1) of Lifson and McClintock (1966) which is probably correct for a study of ~ 18 g hatchlings. However, in  32  attempting to determine the metabolic costs of three distinct phases of hatchling dispersal the experimental design was perhaps outside the attainable scope (i.e., capabilities) of the DLW method. For instance, Jones et al. (2007) found 176 kJ kg-1 day-1 for the maximum metabolic rate (MMR) in frenzied swimming by olive ridleys (measured by respirometry), a value 4.6 times lower than Trullas et al. (2006) found for swimming MR estimated by DLW (812 kJ kg-1 day-1).  Isotopes injected intra-venously (IV) have the shortest time to equilibration with the subject‘s body water. Intra-muscular/intra-peritoneal (IM/IP) injections are intermediate while oral dosing produces the longest times to equilibration. Other factors such as metabolic rate and body mass also affect time to equilibration, for instance smaller animals with higher metabolic rates have shortened equilibration times. As marine turtles do not have a peritoneal cavity we use the term IC to indicate intra-coelemic and IC injections in turtles are equivalent to IP in other species. Speakman (1997) derived an equation to determine time to equilibration for IM/IP injections (equilibration time in hours=2.555+0.360loge) based on data from 41 studies on mammals, marsupials, birds, and reptiles ranging in mass from 2.6 g to 108 kg. Intra-venous injections accelerate and oral dosing retards the time to equilibration as derived from the above equation. Our green turtles, mass 22.42 ± 3.13 kg, injected IC, had equilibration times of ~ 5 hours (Fig. 2.1). This compares with a time of 3.7 hours from the Speakman (1997) equilibration time equation and the increase in time (1.3 hours) for equilibration in our study is probably due to the lower metabolic rate of reptiles as the equation is derived from 41 species but only 2 are reptilian so it is biased towards animals with higher MRs. Southwood et al. (2006) used 12 hours postinjection for their equilibration sample of IC injected green turtles (15.9 ± 4.7 kg) and did not perform an equilibration time curve. According to the equation above and our equilibration time  33  curve for green turtles (Fig. 2.1), equilibration for Southwood et al. should have occurred within 3.6-5 hours post-injection. Therefore, at 12 hours post-injection their sample is probably on the washout curve, leading to an overestimate of the body water pool and consequently overestimation of CO2 production. Wallace et al. (2005) injected leatherback turtles IV and their equilibration time curve suggests isotopes equilibrated with the turtles‘ body water in 2-4 hours post-injection. IV injections in large marine mammals equilibrate in 1-3 hours (Lydersen et al., 1992; Aquarone, 2004) and a somewhat longer equilibrium time is expected in reptiles.  Trullas et al. (2006) injected ~18 g hatchlings IV with DLW and took the equilibration sample 2 hours later. They based the equilibration time of 2 hours on Speakman‘s equation and added an hour as they were using reptiles. Nagy and Knight (1989) found 16.6 – 19.5 g geckos and skinks equilibrated in 1 hour with IP injections. As previously mentioned Wallace et al. (2005) found ~268 kg leatherbacks equilibrated with body water in 2-4 hours for IV injections thus Trullas et al. (2006) probably took their equilibration sample on the washout curve and therefore overestimated IDS and subsequently CO2 production. Their TBW measurements confirm this because the hatchlings were injected as they emerged from the nest or incubator, before ingesting any water, yet TBWs were 85% - values usually associated with well hydrated turtles (see Ortiz et al., 2001; Wallace et al., 2005; Southwood et al., 2006; this study, Table 2.1). Turtles emerge from the nest dehydrated and drink seawater upon entering the ocean (Reina et al., 2002). For hatchlings of this size we suggest that the intercept method to determine IDS from body water equilibration is more appropriate (see Speakman, 1997) than the plateau method as it is hard to take multiple blood samples from < 100 g animals. All sea turtle species are considered  34  threatened or endangered which calls into question the use of the whole body desiccation method for determination of TBW.  The DLW method gave negative MR determinations for fasted turtles (Table 2.1). Five of six turtles had negative MRs and the one positive MR value was 88% less than the respirometry MR value. Speakman (personal communication) suggested that isotopes sequestered during the fed trials could be released during the fasting trial. Therefore, we conducted a revised fasting trial, fasting two turtles for 10 days then injecting with isotope just before the start of the fasting trial. We again obtained negative MRs (-9.75 ± 5.18 kJ kg-1 day-1). The most obvious explanation for the error in DLW-derived MRs for fasted turtles is the 52% drop in MR (based on oxygenconsumption) with fasting (28.66 ± 5.31 to 13.77 ± 1.49 kJ kg-1 day-1) while there is only a 36% drop in water flux (9.57 ± 1.33 to 6.14 ± 0.65 % TBW day-1) thus leading to a fasting kd:ko ratio of 1.08 ± 0.16. Deuterium washout (kd) was greater than ko for four of the six turtles (Table 2.1). Southwood et al.‘s (2006) field study had the lowest kd:ko ratios (i.e., 0.81 ± 0.03 and 0.84 ± 0.02 for summer and winter, respectively) of any marine turtle study to date. Obviously, the study turtles did not have the typical low MR accompanied with high water turnover as seen in the other marine turtle studies (Wallace et al., 2005; Trullas et al., 2006; this study). Water turnover of green turtles is lower than other marine turtles (Ortiz et al., 2001; Wallace et al., 2005; Table 2.1) and actively foraging green turtles probably have an elevated mass specific MR compared with internesting leatherbacks (Wallace et al., 2005) and our captive green turtles with limited mobility.  35  Wallace et al. (2005) did not report any negative MRs but could not calculate MR for two turtles due to high kd:ko ratios. Leatherbacks have the highest water flux values of marine turtles and Wallace and Jones (2008) have recently shown that, contrary to popular belief, leatherback MR is not elevated relative to other marine turtles. Wallace et al. (2005), however, did find that leatherbacks remained active (swimming) during the interesting period and this may have been enough to keep the MR to water flux ratio in check (kd:ko=0.7, 0.86, 0.92, and 0.93 in the other four animals).  Another source of error in Wallace et al.‘s determination of leatherback MR may lie in selection of 0.7 for RQ in internesting leatherbacks. Evidence from other studies suggests that leatherbacks forage during the internesting interval (Southwood et al., 2005; Fossette et al., 2008). If leatherbacks were ingesting prey then, as RQ is higher for feeding animals, this could lead to an overestimation of MR. On the other hand, if turtles are fasting then perhaps RQ=0.7 is too high. We measured RER < 0.7 for fasted turtles in our study (Table 2.1). These low RERs however could be the result of respiratory derived CO2 being incorporated in the urine as ammonium bicarbonate, buffering ammonia excretion, but whether this even occurs in leatherbacks is unknown.  The DLW method accurately measures MR in the 3 non-Chelonian members of the marine reptile group. For instance, marine iguanas and crocodiles have water flux rates < 10% TBW day-1 and MRs of 30-70 kJ kg-1 day-1 (Nagy and Shoemaker, 1984; Christian et al., 1996; Drent et al. 1999). While there is no record of DLW use in sea snakes their water flux is as low as 1.2% TBW day-1 (Schmidt-Nielsen and Skadhauge, 1967). Freshwater reptiles however are probably  36  bad candidates for DLW studies. Booth (2002) found freshwater turtles turnover their body water 1.6 to 4.3 times per day (160-430% TBW day-1) and concluded that the use of DLW is impractical, unless the freshwater turtles are hauling over land or in terrestrial estivation (Roe et al., 2008) when water turnover rates will be reduced.  Green turtles in this study showed a significant decrease in TBW content when fasted. Yet there was not a significant drop in body mass (Table 2.1). There was a trend for a ~ 600 g drop in body mass but this does not account for all the water lost if TBW decreased 8%. However, a turtle body mass measurement may include water in the intestinal tract and bladder that is temporarily stored or moving through the turtle and is not incorporated in the TBW measurement. This is perplexing and we do not have a concrete explanation. Water turnover rates decreased with fasting and then returned to normal levels post-fasting or showed a compensatory increase for the first 24 hours post-fasting. Green turtle water flux rates are low compared with other marine turtles. Southwood et al. (2006) found green turtles had water flux rates of 6 to 8% TBW day-1 which corresponds with our finding of 6 to 10% TBW day-1. Ortiz et al. (2000) found that Kemp‘s ridley turtles have flux rates of 16% TBW day-1 while for leatherbacks water flux is as high as 24% TBW day-1 (Wallace et al., 2005). Hatchling water flux rates can be anywhere from 20 to 90% TBW day-1 for green, leatherback, and olive ridley turtles (Reina, 2000; Reina et al., 2002; Wallace et al., 2005). The differences in adult water flux rates are most likely due to water content of diet and MRs (e.g., how active the turtle is, how much food intake per day, how rapidly wastes are voided).  37  Our green turtle RER during feeding was 0.83 ± 0.07, which implies a combination of fat, carbohydrate, and protein burning. The fasted turtles, however, had RERs lower than expected (0.53 ± 0.18, 0.59 ± 0.09; Table 2.1). RERs less than 0.7 may be due to the production of uric acid in the excreta or gluconeogenesis from fat (Kleiber, 1961). On the other hand Coulson and Hernandez (1964) found that low RER measurements in alligators could be due to ammonium bicarbonate, in the urine, being derived from respiratory CO2 thus reducing CO2 excretion from the lungs. A similar observation was made by Grigg (1978) in crocodiles. Interestingly, we only recorded low RERs during fasting. If the decreased RER was due to CO2 excretion in urine then we would expect a low RER for fed turtles as well as fasting, suggesting that gluconeogenesis from fat (during fasting) is the probable explanation. This leaves the researcher using the DLW method on reptiles with a conundrum: what RQ to use for fasted animals? In our study the point was moot as fasting trials gave negative DLW-derived MRs no matter whether an RER of 0.53 or 0.70-1.0 was used. Researchers working with reptiles and DLW should determine RER in either pre- or post-study validation experiments. Furthermore, urine bicarbonate levels should be tested as well to determine if these are from respiratory CO2. Marine turtles are capable of urea, uric acid, and ammonia excretion (Khahil, 1947) thus a further issue arises as these animals may shift their nitrogenous waste biochemistry and change RER depending on their situation (i.e., in salt water or respirometer dry box). However, as we found that DLW does not work in fasting turtles the technique should only be used in foraging turtles which are in a steady-state (i.e., energy intake equals energy output) and RER should be measured or derived for the diet.  Interestingly there was no significant difference in breathing frequency for fed and fasted turtles (0.10 ± 0.02 and 0.09 ± 0.03 breaths min-1, respectively). Yet, there was a 52% drop in MR with  38  fasting. This suggests that the turtles decreased tidal volume or oxygen extraction efficiency. Turtles lowering oxygen extraction efficiency by shunting blood away from the pulmonary system would reduce PO2 and increase PCO2 in systemic blood signaling the turtle to breath even though it would still have ample lung stores (causing the turtle to maintain an increased breathing frequency despite the MR drop). Alternatviely, turtles could simply decrease tidal volume in response to lowered metabolic demand. With decreased O2 stores their internal chemoreceptors would signal them to breath causing them to surface with the same frequency as fed turtles even with lowered oxygen demand.  CONCLUSIONS This study shows that the DLW method gives valid MR determinations in marine turtles if certain criteria are met. For instance, the DLW method should not be used to estimate the MR of an individual and turtles should preferably be in steady-state or positive energy balance where energy input is equal to or greater than energy output. It is imperative that the turtles‘ water flux rates are moderate and the turtles are active thus reducing the ratio of deuterium isotope turnover to oxygen-18 isotope turnover (reducing kd:ko). DLW method validation should be performed on a species level basis and RER should be measured or based on a known diet. Changing TBW and RQ issues arise with fasting turtles causing complications in IDS and energy calculations, respectively. If low RERs are measured further research should be done to determine the cause and whether urine bicarbonate is being derived from respiratory CO2. Furthermore, if urine bicarbonate is found this could affect fractionation factors, as CO2 dissolved in liquid is not fractionated from body water with the same isotopic proportions as expired CO2. And finally researchers publishing papers using DLW should give complete details on individual animals for 39  injectate enrichment and dose, IDS, No & Nd, as well as ko & kd, so that values can be recalculated as information on equations, isotope dilution space ratio, and fractionation advance. In this regard we have attempted to make our experimental design and results transparent in hopes that future researchers may re-work the numbers as information on DLW techniques evolve.  40  Table 2.1 Mass (fasted is the average between days 10 & 15; post-fast 21 days is 21 days from start of trial and so on), background isotope levels, injectate details, isotope dilution space, washout ratios, metabolic rate (DLW MRs from equation 7, see Methods), and water turnover rate for 6 green turtles used in DLW validation. Mean ± standard deviation and significant differences are given among related data groups (i.e., values within the lines of sig. diff. column). In the final column letters (a,b,c) are used to denote significant differences. If the same letter is given then there is no difference, i.e. ‗a‘ is significantly different from ‗b‘ and ‗c‘ but not from another ‗a‘. Turtle  L1  L2  L3  L4  R1  R4  Mean  s. d.  sig. diff.  fed fasted post-fast 21 d post-fast 23 d post-fast 25 d  19.60 19.04 19.48 19.42 19.92  25.04 25.18 25.06 25.28 25.38  19.76 19.10 19.44 19.92 20.10  25.88 25.30 25.50 26.20 26.20  24.84 24.10 24.38 24.92 25.22  19.38 19.20 19.46 19.82 19.90  22.42 21.99 22.22 22.59 22.79  3.13 3.18 3.04 3.18 3.10  a a a a a  Mass (kg)  Background 2H  153.85  154.59  153.82  153.57  153.21  153.12  153.69  0.53  Background 18O Initial Injectate (moles) Injectate enrichment 2 H APE  1994.64 0.37  1995.18 0.55  1994.21 0.42  1994.67 0.52  1994.82 0.53  1994.51 0.42  1994.67  0.32  260611.09  260611.09  260611.09  260611.09  260611.09  260611.09  Nd (ml)  695197.53 14047.42  695197.53 17593.36  695197.53 14821.41  695197.53 16709.71  695197.53 17019.68  695197.53 14014.79  15701.06  1593.03  No (ml)  13398.16  16887.24  14152.22  15862.79  16365.00  13608.91  15045.72  1508.41  1.048  1.042  1.047  1.053  1.040  1.030  1.048  0.005  0.07  0.10  0.08  0.08  0.09  0.07  0.08  0.01  0.08  0.01  18  O APE  Dilution ratio Nd:No 10 day fast 2H boost Injectate (moles) 15 day fast 2H boost Injectate (moles) 2 H boost enrichment 2 H APE  0.08  0.09  0.07  0.08  0.08  0.07  952572.52  952572.52  952572.52  952572.52  952572.52  952572.52  Fed - k d  6.399E-05  6.154E-05  8.009E-05  6.024E-05  5.836E-05  4.913E-05  6.223E-05  1.013E-05  Fed - k o  7.297E-05  6.452E-05  8.653E-05  6.704E-05  6.298E-05  5.143E-05  6.758E-05  1.166E-05  Fasted - k d  4.832E-05  3.799E-05  4.057E-05  3.925E-05  4.786E-05  4.218E-05  4.270E-05  4.406E-06  Fasted - k o  4.462E-05 0.88 1.08  3.969E-05 0.95* 0.96  3.997E-05 0.93 1.02  3.616E-05 0.90 1.09  3.500E-05 0.93 1.37  4.490E-05 0.96* 0.94  4.006E-05 0.91 1.08  4.126E-06 0.02 0.16  a b a  Fed - kd :ko Fasted - kd :ko -1  -1  Fed-respirometry (kJ kg day )  32.70  -  33.80  23.98  24.17  -  28.66  5.31  Fed-DLW (kJ kg-1 day-1)  59.56  neg. value  19.22  32.78  11.85  neg. value  30.85  21.01  a  Fasted-respirometry (kJ kg-1 day-1)  15.89  14.40  12.43  14.17  11.73  14.01  13.77  1.49  b  Fasted-DLW (kJ kg-1 day-1)  -65.80  -6.60  -34.19  -44.01  -130.35  1.63  -46.55  47.90  n/a  fed - RER fast (10 d) - RER fast (15 d) - RER  0.88 0.58 0.69  0.82 0.60 0.69  0.78 0.24 0.58  0.93 0.46 0.58  0.74 0.50 0.48  0.85 0.78 0.52  0.83 0.53 0.59  0.07 0.18 0.09  a b b  fed - breathing frequency fasted - breathing frequency  0.14 0.11  0.09 0.06  0.1 0.09  0.09 0.08  0.1 0.06  0.07 0.12  0.10 0.09  0.02 0.03  a a  70.31 61.96 63.19  69.87 65.10 63.50  74.03 69.13 64.66  64.37 50.19 56.07  68.13 52.70 52.71  69.79 67.78 64.48  69.64 61.14 60.77  3.98 7.95 5.09  a b b  1.29 0.83 0.83 1.64 1.38  1.56 0.90 0.87 1.50 1.31  1.71 0.78 0.71 1.75 1.75  1.45 0.72 0.80 1.62 1.41  1.43 0.87 0.87 1.41 1.42  0.99 0.80 0.74 1.30 1.16  1.50 0.82 0.80 1.54 1.40  0.18 0.07 0.07 0.17 0.19  a b b a a  9.21 7.00 6.94 13.74 9.82  8.86 5.45 5.48 9.46 7.47  11.53 5.82 5.83 14.34 11.79  8.68 5.64 5.68 11.52 8.41  8.40 6.87 6.86 11.14 8.35  7.08 6.07 6.07 10.65 8.25  9.57 6.14 6.14 11.81 9.02  1.33 0.65 0.62 1.87 1.56  a b b c a  % TBW (2H) fed fasted (10 d) Fasted (15 d) Water turnover -1 L day fed fasted (10 d) fasted (15 d) fed (21 d) fed (25 d) -1 % TBW day fed fasted (10 d) fasted (15 d) fed (21 d) fed (25 d)  41  Table 2.2 Body pool estimate (N) and DLW derived metabolic rate (MR) for the 7 equations listed in the methods section for the fed trials. MRs are from the DLW two-sample technique (Speakman, 1997). Final column gives the absolute percent difference between the DLW derived MR and the respirometry derived MR. For equation 3 the body pool estimate (No & Nd) are used for each individual turtle and these can be found in Table 2.1 Equation 1 2 3 4 5 6 7  Pool(s) 1 1 2 2 2 2 2  N (ml) 15045.72 15045.72 table 1 14996.96 14902.35 15045.72 15045.72  s.d. 1508.41 1508.41 table 1 1511.58 1501.99 1508.41 1508.41  -1  -1  MR (kJ kg day ) 53.07 66.91 16.4 51.79 42.13 38.5 30.85  s.d. 20.98 21.56 19.83 20.13 19.83 19.77 21.01  % diff. from resp. 85.17 133.46 42.78 80.70 47.00 34.33 7.67  42  oxygen-18 enrichment (p.p.m.)  2500  2300  Equilibration sample time ~ 5 hours post DLW injection. 2100  1900  deuterium enrichment (p.p.m.)  360  320  280  240  200 Turtle L1 Turtle L3 Turtle R4  160  120 0  2  4  6  8  10  12  Time (hours)  Figure 2.1 Isotopic enrichment values of the equilibration time course for deuterium (2H) and oxygen-18 (18O) of three green turtles (Chelonia mydas). These enrichments represent the background enrichment levels (day 0) and the enrichment levels post intra-coelomic injection and during the plateau period. 43  oxygen-18 enrichment (p.p.m.)  2500 Turtle L1 Turtle L3 Turtle R4 Turtle L4 Turtle L2 Turtle R1  2300  fed trial  2100  fasting fasting trial  1900 400 2  deuterium enrichment (p.p.m.)  H reboost  350  300  250  200  150 0  5  10  15  20  Time (days)  Figure 2.2 Isotopic enrichment values for deuterium (2H) and oxygen-18 (18O) during the course of the DLW validation experiment for 6 green turtles (Chelonia mydas). Background enrichment levels given at day 0, equilibration, fed DLW trials (days 1 – 6), fasting period (days 6 – 16), deuterium reboost (day 16), fasting DLW trials (days 16 – 21), and final deuterium reboost (day 21). 44  5.2 Loge oxygen-18 & deuterium (p.p.m.)  Fasting DLW trials  4.8  4.4  L1; oxygen-18 L1; deuterium L3; oxygen-18 L3; deuterium  4.0  R4; oxygen-18 R4; deuterium L4; oxygen-18 L4; deuterium L2; oxygen-18  6.2  L2; deuterium  Fed DLW trials  R1; oxygen-18  Loge oxygen-18 & deuterium (p.p.m.)  R1; deuterium  5.8  5.4  5.0  4.6  4.2 0  1  2  3  4  5  Time (days)  Figure 2.3 Natural log of isotopic enrichment above background levels used for DLW metabolic measurements using the Multiple-Sample Approach. The upper graph depicts the washout during the fasting trials, lower graph during fed trials. Solid lines are for 18O and dashed lines for 2H. 45  REFERENCES Anderson NL, Hetherington TE, Williams JB (2003) Validation of the doubly labeled water method under low and high humidity to estimate metabolic rate and water flux in a tropical snake (Boiga irregularis). J. Appl. Physiol. 95: 184–191 Aquarone M (2004) Body composition, field metabolic rate, and feeding ecology of walrus (Odobenus rosmarus) in northeast Greenland. PhD thesis, National Environmental Research Institute, Ministry of the Environment, Denmark Bjorndal KA (1996) Foraging ecology and nutrition of sea turtles. In The Biology of Sea Turtles (ed. P. L. Lutz and J. Musick), pp. 199-231. CRC Press, Boca Raton Booth DT (2002) The doubly-labeled water technique is impractical for measurement of field metabolic rate in freshwater turtles. Herp. Rev. 33: 105-107 Butler PJ, Green JA, Boyd IL, Speakman JR (2004) Measuring metabolic rate in the field: the pros and cons of the doubly labelled water and heart rate methods. Funct. Ecol. 18: 168–183 Christian K, Green B, Kennett R (1996) Some physiological consequences of estivation by freshwater crocodiles, Crocodylus johnstoni. J Herp. 30: 1-9 Coulson RA, Hernandez T (1964) Biochemistry of the Alligator: a Study of Metabolism in Slow Motion. Louisiana State University Press, Baton Rouge Coward WA, Prentice MA, Murgatroyd PR, et al. (1985) Measurement of CO2 and water production rates in man using 2H, 18O labeled H2O: comparisons between calorimeter and isotope values, in Human Energy Metabolism: Physical Activity and Energy Expenditure Measurements in Epidemiological Research Based upon Direct and Indirect Calorimetry (ed. A.J.H. van Es), vol. 5, pp. 126-8. Eur. Nutr. Rep., CIP-gegevens Koninklijke Bibliotheek, The Hague 46  Davenport J (1998) Sustaining endothermy on a diet of cold jelly: energetics of the leatherback turtle Dermochelys coriacea. British Herp. Soc. Bull. 62: 4-8 Drent J, van Marken Lichtenbelt WD, Wikelski M (1999) Effects of Foraging Mode on the Energetics of the Marine Iguana, Amblyrhyncus cristalus. Functional Ecology 13: 493-499 Fedak MA, Rome L, Seeherman HJ (1981) One-step N2-dilution technique for calibrating opencircuit VO2 measuring systems. J. Appl. Physiol. 51(3): 772-776 Fossette S, Corbel H, Gaspar P, LeMaho Y, Georges J (2008) An alternative technique for the long-term satellite tracking of leatherback turtles. Endangered Species Research 4: 33-41 Grigg GC (1978) Metabolic rate, Q10 and respiratory quotient (RQ) in Crocodylus porosus, and some generalizations about low RQ in reptiles. Physiol. Zool. 51: 354-360 Jones TT, Reina RD, Darveau C-A, Lutz PL (2007) Ontogeny of energetics in leatherback (Dermochelys coriacea) and olive ridley (Lepidochelys olivacea) sea turtle hatchlings. Comp. Biochem. Physiol. Part A 147: 313–322 Khalil F (1947) Excretion in reptiles – 1. Non-protein nitrogen constituents of the urine of the sea turtle, Chelonia mydas (L.). J. Biol. Chem. 171: 611-616 Kleiber M (1961) The Fire of Life: an Introduction to Animal Energetics. John Wiley and Sons, Inc., New York Kraemer JE, Bennett SH (1981) Utilization of posthatching yolk in loggerhead sea turtles, Caretta caretta. Copeia 1981(2): 406-411 Lifson N, McClintock R (1966) Theory of use of the turnover rates of body water for measuring energy and material balance. J. Theor. Biol. 12: 46–74 Lutcavage M, Lutz PL (1986) Metabolic rate and food energy requirements of the leatherback sea turtle, Dermochelys coriaceia. Copeia 1986(3): 796-798  47  Lydersen C, Griffiths D, Gjertz I, Wiig O (1992) A tritiated water experiment on a male Atlantic walrus (Odobemus rosmarus rosmarus). Mar. Mammal Sci. 8: 418-420 Nagy KA (1980) CO2 production in animals: analysis of potential errors in the doubly labeled water method. Am. J. Physiol. 238: R466–73 Nagy KA (1983) The Doubly LabeledWater (3HH18O) Method: A Guide to Its Use. Los Angeles: Univ. Calif. Publ. No. 12-1417 Nagy KA (1983) Ecological energetics. In Lizard Ecology: Studies of a Model Organism (ed. R. B. Huey, E. R. Pianka and T. W. Schoener), pp. 24–54. Cambridge, MA: Harvard Univ. Press Nagy KA (1989) Doubly-labeled water studies of vertebrate physiological ecology. In: Rundel PW, Ehleringer JR, Nagy KA (eds) Stable Isotopes in Ecological Research. Springer-Verlag, New York pp. 270–87 Nagy KA, Shoemaker VH (1984) Field energetics and food consumption of the Galapagos marine iguana, Amblyrhyncus cristatus. Physiol. Zool. 57: 281–90 Nagy KA, Knight MH (1989) Comparative field energetics of a kalahari skink (Mahuya striata) and gecko (Pachydactylus bibroni). Copeia 1989: 13-17 Nagy KA, Girard IA, Brown TK (1999) Energetics of free-ranging mammals, reptiles, and birds. Annu. Rev. Nutr. 19: 247-277 Ortiz RM, Patterson RM, Wade CE, Byers FM (2000) Effects of acute fresh water exposure on water flux rates and osmotic responses in Kemp‘s ridley sea turtles (Lepidochelys kempi). Comp. Biochem. Phys. A 127: 81-87 Reina RD (2000) Salt gland blood flow in the hatchling green turtle, Chelonia mydas. J. Comp. Physiol. Part B 170: 573-580  48  Reina RD, Jones TT, Spotila JR (2002) Salt and water regulation by the leatherback sea turtle Dermochelys coriacea. J. Exp. Bio. 205: 1853-1860 Roe JH, Georges A, Green B (2008) Energy and water flux during terrestrial estivation and overland movement in a freshwater turtle. Physiol. Biochem. Zool. 81(5): 570-583 Schmidt-Nielsen K, Skadhauge E (1967) Function of the excretory system of the crocodile (Crocodylus acutus). Amer. J. Physiol. 212: 973-980 Schoeller DA (1988) Measurement of energy expenditure in free-living humans by using doubly labeled water. J. Nutr. 118: 1278-1289 Schoeller DA, Ravussin E, Schutz Y, Acheson KJ, Baertschi P, Jequier E (1986) Energy expenditure by doubly labeled water: validation in humans and proposed calculation. Am J Physiol 250: R823–R830 Southwood AL, Andrews RD, Paladino FV, Jones DR (2005) Effects of swimming and diving behavior on body temperatures of Pacific leatherbacks in tropical seas. Physiol. Biochem. Zool. 78: 285–297 Southwood AL, Reina RD, Jones VS, Speakman JR, Jones DR (2006) Seasonal metabolism of juvenile green turtles (Chelonia mydas) at Heron Island, Australia. Can. J. Zool. 84: 125–135 Speakman JR (1993) How should we calculate CO2 production in DLW studies of animals? Funct. Ecol. 7: 746-750 Speakman JR, Nair KS, Goran MI (1993) Revised equations for calculating CO2 production from doubly labelled water in humans. Am. J. Physiol. 264: E912 – E917 Speakman JR (1997) Doubly Labelled Water: Theory and Practice. London: Chapman & Hall. pp. 399 Thorson TB (1968) Body fluid partitioning in reptilia. Copeia 3: 592-601  49  Trullas S, Spotila JR, Paladino FV (2006) Energetics during hatchling dispersal of the olive ridley turtle Lepidochelys olivacea using doubly labeled water. Physiol. Biochem. Zool. 79: 389–399 Wallace BP, Williams CL, Paladino FV, Morreale SJ, Lindstrom RT, Spotila JR (2005) Bioenergetics and diving activity of interesting leatherback turtles Dermochelys coriacea at Parque Nacional Marino Las Baulas, Costa Rica. J. Exp. Biol. 208: 3873–3884 Wallace BP, Jones TT (2008) What makes marine turtles go: A review of metabolic rates and their consequences. J. Exp. Mar. Biol. Ecol. 356: 8-24  50  CHAPTER THREE: GROWTH OF CAPTIVE LEATHERBACK TURTLES DERMOCHELYS CORIACEA WITH INFERENCES ON GROWTH IN THE WILD: IMPLICATION FOR FISHERIES INDUCED POPULATION DECLINE2 INTRODUCTION Growth rate may be the single most important measure aiding our understanding of marine turtle demography. Growth rates are crucial for understanding life history aspects such as age at maturity, the temporal duration of various life-history stages, and life stage specific mortality factors. Most studies of growth of marine turtles have focused on the cheloniid species (see Chaloupka & Musick 1997, for review), with few investigating growth in leatherbacks (Rhodin 1985, Rhodin et al. 1996, Zug and Parham 1996, Price et al. 2004). This is not surprising considering the almost exclusively oceanic-pelagic lifestyle of leatherbacks (only females go on land for nesting) and the extreme difficulty of maintaining them in captivity (Birkenmeier 1971, Jones et al. 2000). Estimates of growth are urgently needed to help conserve leatherback turtles which are listed as critically endangered (IUCN 2007) and may be nearing extinction in the Pacific (Spotila et al. 1996, 2000).  Leatherbacks are the largest of the marine turtles (Buskirk & Crowder 1994) and perhaps the fastest growing (Rhodin 1985, Rhodin et al. 1996). There are two studies on growth rates of  2  A version of this chapter is in review for publication. Jones TT, Hastings MD, Bostrom BL, Pauly D, Jones DR. 51  adult leatherbacks (Zug & Parham 1996, Price et al. 2004) and growth rate data for juvenile leatherbacks in nature are non-existent as their distribution is largely unknown, making markrecapture methods for measurements of growth extremely difficult. Mark-recapture data from other marine turtles have shown that loggerheads and greens reach sexual maturity at ≥ 15 years and ≥ 20 years respectively (Chaloupka & Musick 1997). Other studies using new or refined techniques even push the age-at-maturity to 30-40 years for various populations of loggerheads and greens (Zug et al. 2002, Heppell et al. 2003), and > 40 years for the Hawaiian green turtle (Balazs & Chaloupka 2004). Extremely rapid growth rates in captive leatherbacks have led to the speculation that these animals reach sexual maturity as quickly as 2-3 years (Frayr 1970, Spoczynska 1970, Birkenmeier 1971, Foster & Chapman 1975, Phillips 1977, Witham 1977). However, many of these studies are based on sample sizes of a single individual (from > 2months of age on), and do not account for growth rate declining with age.  Rhodin (1985) predicted an age at maturity of 3-6 years based on chondro-osseous (cartilage and bone) morphology of leatherbacks. However, more recent skeletochronological analyses suggest that leatherbacks could take as long as 13-14 years to sexually mature (Zug & Parham 1996). Dutton et al. (2005) suggested that leatherbacks reach maturity in 12-14 years based on greatly increased returns at a nesting beach (St. Croix) after intensive beach protection and nest relocation that increased hatchling production by an order of magnitude. Genetic analysis (from the same site) suggests that current first-time nesters are the genetic offspring of leatherbacks nesting in the 1980s, again pointing to an age-at-maturity estimate of < 20 years.  52  Given the paucity of data and the large span in current age-at-maturity estimates, more robust growth rate data are needed, with rearing studies probably presenting the only option to clarify important aspects of leatherback life-history. Maintaining leatherbacks in captivity, however, is no easy task. Due to the leatherback‘s completely pelagic lifestyle this species never adapts to the confines of a tank, resulting in animals injuring themselves against obstructions such as tank walls and bottom (Berkenmeier 1971, Foster & Chapman 1975, Phillips 1977, Witham 1977, Bels et al. 1988, Chan 1988). The resulting skin abrasions become infected and generally lead to the death of the animal (Berkenmeier 1971, Jones et al. 2000). For most studies documenting growth or other life parameters of turtles in captivity, authors were unable to rear specimens past 100 days due to infections or their complications (Deraniyagala 1939, Frayr 1970, Spozynska 1970, Berkenmeier 1971, Witham 1977, Bels et al. 1988, Jones et al. 2000). Maintaining high water quality and finding an acceptable diet for the obligate jelly feeders presents additional challenges to rearing leatherbacks (Berkenmeier 1971, Jones et al. 2000).  Herein, we: 1) describe the approach used to allow for juvenile leatherbacks to be reared in captivity for over two years, 2) compare the length-mass relationships of our captive animals with other captive leatherbacks, wild juveniles and adults, 3) describe the growth rate of leatherbacks during the first years, 4) derive parameters of the von Bertalanffy (VBGF; von Bertalanffy 1938), Gompertz and logistic growth functions (Ricklefs 1967) for growth in leatherbacks based on juvenile captive stock and adult skeletochronological data, and 5) compare the growth curves to determine the validity of the output.  53  MATERIALS & METHODS Twenty hatchlings (emergence July 2nd, 2005) were transported by air, from Tortola, British Virgin Islands (BVI) to the Animal Care Center, Department of Zoology, University of British Columbia, under Canada CITES Import permit CA05CWIM0039 and British Virgin Islands CITES Export certificate CFD062005. These animals were housed and maintained for research purposes in accordance with animal care standards of the Canadian Council for Animal Care (CCAC) and the UBC Animal Care Committee (UBC Animal Care Protocol: A04-0323).  Animal husbandry The three main constraints on rearing leatherbacks are: (a) their oceanic-pelagic nature (no recognition of barriers), (b) water quality, and (c) designing a food matching their gelatinous diet in the wild.  (a), As leatherbacks are oceanic-pelagic animals that swim continuously and do not recognize vertical and horizontal barriers (tank walls & bottom), they were tethered to PVCTM pipes secured across the top of the holding tanks. Animals < 10 kg were attached to the pipes by a tether of monofilament fishing line with swivels. VelcroTM coupling patches were used, one tied to the end of the fishing line while the complementary patch was glued to the posterior portion of the carapace. Each hatchling could swim or dive in any direction, but was unable to contact other turtles or the bottom and walls of the tank. Juveniles ≥ 10 kg were secured to the tether with a harness made of TygonTM tubing. The harness circled each shoulder like the straps of a backpack and looped around the caudal peduncle.  54  (b), Turtles were maintained in large oval tanks (5m long x 1.5 m wide x 0.3 m deep) containing ~ 2,500 l of re-circulated/filtered salt water. Once a week the tanks were drained and scrubbed with Quatricide® PV (Pharmacal Research Laboratories Inc., Naugatuck, CT USA) a fungicide, bactericide and virucide. The tanks were re-filled with new water delivered from the Vancouver Aquarium and Marine Science Center, having a zero coliform bacterial count upon arrival. As the turtles grew in size, header tanks were added that doubled or tripled the volume of filtered water per turtle. The water temperature was maintained at 24 ± 1 oC. Four fluorescent fixtures (40 W UVA/B; Repti-Glow® 8) suspended 0.5 m above each tank provided full spectrum radiation for 12 hours per day; each tank was also exposed to ambient light. Water quality was maintained between the following levels: pH = 8.0-8.3; salinity = 28-33 ppt; and ammonia < 0.1 mg L-1. Water quality for each tank was controlled by four systems: (i) a biological/mechanical filter (built by UBC – Zoology Workshop staff) containing bio-balls™ and fiberglass matting; (ii) a protein skimmer (Red Sea Berlin XL Turbo Skimmer, Houston TX, USA); (iii) a sand filter (TRITON® II TR 100; Pentair Pool Products™, Sanford, North Carolina, USA) designed for large volumes of water; and (iv) an ultraviolet filter (Aqua Ultraviolet™ 114 W UV water sterilizer, CA USA).  (c), It was necessary to make food of the proper texture and consistency since the diet of leatherbacks in nature consists solely of gelatinous zooplankton (e.g., jellyfish). Furthermore, feeding leatherbacks a diet of non soft-bodied marine organisms, such as fish, has been shown to cause gut impaction and death in leatherbacks (Foster & Chapman 1975, Witham 1977). A diet of squid and agar was successfully used by Chan (1988) and our diet consisted of squid (Pacific Ocean squid; mantle and tentacles only), vitamins (ReptaviteTM), and calcium (Rep-CalTM),  55  blended with unflavored gelatin liquefied in hot water. The molten food was poured into trays lined with waxed paper and refrigerated until solid. The solidified food was cut into strips. Turtles were fed 3 to 5 times daily to satiation during the first 2-months of age, and 3 times daily to satiation when > 2-months of age.  Data collection and analysis The turtles were weighed and measured on emergence, at 3 and 7 days of age and then weekly (Table 3.1). Straight carapace length (SCL), the distance from the center of the nuchal notch to the caudal peduncle (posterior of the carapace), was determined with a digital caliper to the nearest 0.1 mm. The turtles were weighed using an Ek-1200 A scale from hatching to body mass of 1.2 kg (± 0.001 kg), and an ADAM CPW-60 scale (Dynamic Scales, 1466 South 8th Street, Terre Haute, IN 47802) for mass > 1.2 kg (± 0.02 kg). All averages are given ± 1 Standard Deviation.  For purposes of comparison with others growth studies, all cited studies using curved carapace length (CCL) were converted to SCL using the equation of Tucker & Frazer (1991):  eq.1)   CCL  SCL     2.04  1.04   We made comparisons of the length-mass (L-M) data pairs of the leatherbacks in this study and with other captive turtles (Table 3.2), wild juveniles (Table 3.3), and adults (Deraniyagala 1939, Eckert et al. 1989, Boulon et al. 1996, James et al. 2005, Georges and Fossette 2006, B. Wallace pers. comm.) by fitting the data pairs to the logarithmic form of the allometric growth equation:  56  eq. 2)  logM   loga   b  logL  in which M = body mass in kg, L = SCL in cm , ‗a‘ is the proportionality coefficient (intercept on the log;log plot) and ‗b‘ is the body mass exponent (slope of the line).  Growth rate was determined as ΔL/Δt or (L2-L1)/(t2-t1) and given as the growth rate for the midlength of the starting and ending carapace length (L1+L2)/2. The growth rates and corresponding mid-length were then divided up into 10 cm length bins, i.e., < 10 cm SCL, 10 to 20 cm SCL, etc (Table 4) and an average growth rate and mid-length were computed for each bin.  Length-at-age data from Table 3.2 and from skeletochronological studies on adults (Zug & Parham 1996) were also analyzed using the von Bertalanffy, Gompertz, and logistic growth functions. The von Bertalanffy (eq. 3), Gompertz (eq. 4), and logistic (eq. 5) growth functions for length have the form:      eq. 3)  Lt  L 1  e  k t t0   eq.4)  Lt  L e e  eq. 5)  L   Lt     k t  t 0  1  e         k t t  0      57  in which ‗Lt‘ is the predicted length at age ‗t‘, ‗L∞‘ is the mean SCL all adults in the population would reach if they grew indefinitely, ‗k‘ is a growth parameter (not a growth rate) of dimension time-1, and ‗t0‘ is the theoretical age at length = 0 (VBGF only). When hatchlings emerge from nest t = 0, therefore t0 represents embryonic growth duration in the egg. For the Gompertz and logistic growth functions ‗t0‘ is simply a right/left curve shift and does not hold a biological meaning. To determine age-at-maturity from our growth functions we defined maturity as the   5  ln 2  time 95 % of L∞ is obtained, given by 95% L    , (Fabens 1965, Cailliet et al. 1992).  k  The asymptote (L∞) represents mean maximum length and as marine turtles have near zero growth rates after first nesting (Frazer & Ehrhart 1985) then ‗L∞‘ also represents mean population nesting length. However, ‗L∞‘ is the asymptotic length therefore the best-fit curve approaches but never attains ‗L∞‘ thus a point near the asymptote equals mature length (Witzell 1980).  Statistical analysis Each of our turtles, as was also the case with data from the literature, contributed one measurement (the final measurement taken when the animal was in good health) to the L-M relationships and the growth functions to avoid complications of pseudo-replication or nonindependence of data points.  Length-mass relationships (in the form of eq. 2) for this study, data from Table 3.2, as well as juveniles caught in the wild (Table 3.3) and adult data were compared using Student‘s t test to determine if the slopes (b) and intercepts (a) were significantly different. The test statistic was t = 58  (b1–b2)/(Sb1–b2) where ‗b‘ is the slope for the two regressions and ‗Sb1-b2‘ is the standard error between regression coefficients. If no difference was found between the slopes the elevations (yintercept) were then compared where the test statistic was t   Y  1   Y2   bc X 1  X 2   2 1 1 X 1  X 2   s yx c  n  n  A  2 c  1  2  and ‗Ac‘, ‗bc‘, and (s2y.x)c are the common sum of squares, common slope, and common mean square residual, respectively.  Growth rates were compared using a one-way ANOVA and a Tukey-Kramer post-hoc test to determine where significant differences lay. In all statistical analyses alpha was set to 0.05. Student‘s t tests were performed by hand calculations following Biostatistical Analysis by Zar (1999). Growth rate analyses were done on JMP® 4 statistical software program (SAS Institute INC., 2001).  The von Bertalanffy, Gompertz and logistic growth curves were fitted to the data using nonlinear least squares (NLLS) regression (SigmaPlot® 10.0, Systat Software Inc., 2006). Initial parameter limitations were set for k (>0), and t0 (<0). The program performed over 100 iterations (stopping when the change in parameters from consecutive iterations was minimized) before deriving final parameters with standard errors and the best-fit curve. Mean square residual (MSres) and the adjusted coefficient of determination (R2) were compared between the three growth functions to determine which model best represents leatherback growth.  95% confidence and prediction bands are given for both the length-mass relationships and the growth curve. The confidence bands show how well the curve fits the data (i.e., 95% confidence 59  that the true best-fit curve is within the bands). The prediction bands show the scatter of the data (i.e., if more data points were collected 95% of them would be expected to fall within the prediction bands) (Motulsky and Christopoulos 2004).  RESULTS The hatchlings averaged 6.31 ± 0.13 cm SCL and 46 ± 1 g body mass at emergence from the nest (Table 3.1). All hatchlings began feeding on the formulated squid gelatin by 3-5 days after emergence. One turtle lived for 815 days obtaining a mass of 42.65 kg and a length of 72.0 cm SCL. Weekly length and mass measurements taken during the life span of all 20 hatchlings are given in Table 3.1.  Length mass relationships The last L-M data pairs collected from turtles in our study (Table 3.2) gave a L-M regression with the following parameter estimates log y   3.54  2.78  logx  (Figure 3.1A) and closely matched that of leatherbacks from the wild log y   3.63  2.84  logx  (Table 3.3, Figure 3.1B), showing no significant difference in slope or y-intercept (t=0.7099 and 0.084 for slope and y-intercept respectively, p>0.05). Furthermore, there was no significant difference in the slope or y-intercept of L-M data pairs from 4 other captive studies (Table 3.2), 5 adult studies, wild juveniles (Table 3.3), and our study (t=1.824 and 0.1325 for slope and elevation respectively, p>0.05). Consequently, the slope and y-intercept, after pooling all L-M data pairs,  log y   3.67  2.86  logx  is proposed as the standard L-M relationship for leatherback turtles (Figure 3.1C).  60  Growth Growth rate averaged 31.9 ± 2.8 cm year-1 throughout the study period of 1.93 years from emergence (6.31 ± 0.13 cm SCL) through to a juvenile length of 70 cm SCL (Table 3.4). Statistical analysis showed that growth rate slowed significantly at 10 to 20 cm SCL (by 7%) and 40 to 50 cm SCL (by 16%), compensatory growth was not observed after the slow growth phases.  Length-at-age data (Table 3.2) combined with adult length-at-age data from skeletochronology studies (Zug and Parham 1996) fitted by NLLS regression gave the following von Bertalanffy, Gompertz, and logistic growth functions for length respectively,    Lt  141  e e  0.511 t 1.86  , L   1  e 140 t  0.883t  2.6       Lt  143  1  e 0.226t 0.17 ,      (Figures 3.2A, B, C, respectively). Table 3.5 shows the fitted parameters for all three growth functions with standard errors. The VBGF had the lowest MSres. The von Bertalanffy, Gompertz and logistic growth functions predicted an age-at-maturity of 15.3, 6.8, and 3.9 years with a minimum SCL at that age (Lmin) of 121, 110 and 84 cm SCL, respectively (Lmin is given by the lower limit of the 95% prediction band at 95% L∞).  61  DISCUSSION  Length mass relationship Comparison of our length-mass regression (Figure 3.1A) with wild animals (Figure 3.1B, C) suggests that our captive turtles matched the L-M regression of wild juveniles and adult leatherbacks. L-M regressions are commonly used as body condition indices to compare animals of different sizes, where body condition is a term representing the proportion of body tissue carried by an animal in relation to its total length or the length of an arm, wing, snout etc. (Hayes and Shonkwiler 2001). Here, we are using the L-M regression to show that for any carapace length our captive animals are carrying the same proportion of body tissue as wild animals. Using wild animals as the model of proper conditioning allows us to infer to the population at large from studies on our captive stock. Therefore, we feel confident that our growth function is an accurate predictor of natural growth rates and does not represent that of ‗obese‘ over-fed turtles. It should also be noted that leatherbacks from different populations, e.g., Atlantic, Pacific, and Indian Oceans, all fell on the same L-M regression line (Figure 3.1C). Wabnitz and Pauly (2008) have recently shown that the same is true of hard-shelled marine turtles. For instance, Pacific and Atlantic greens and loggerheads fit on the same length-mass relationship, respectively. The values for ‗a‘ (0.00021 and 0.00028, green and loggerhead respectively) and ‗b‘ (2.89 and 2.82, green and loggerhead respectively) are similar to our generalized relationship for leatherbacks (a = 0.00021, b = 2.86) suggesting all marine turtles are constrained by their similar body plans.  62  Growth Our leatherback growth rates per body-length (Table 3.4) averaged 31.98 ± 2.8 cm year-1 for lengths of < 10 cm SCL to 70 cm SCL and compare favorably with those found by Zug & Parham (1996) of 31.6 cm year-1 for juveniles of 8-37 cm SCL (from < 10 to 40 cm SCL our growth rates averaged 32.98 ± 2.33 cm year-1). However, their measurements of 23.1 cm year-1 for juveniles of 37-65 cm SCL are 21% less than our growth rates over the same lengths (29.22 ± 3.47 cm year-1). If leatherbacks were to maintain growth rates of 31.98 ± 2.8 cm year-1 they could attain the minimum nesting length (124 cm SCL) of leatherbacks (Stewart et al. 2007) in 4 years. This same approach led to the assessment that leatherbacks could attain sexual maturity in as little as 2-3 years. For instance, growth rates from captive turtles have been measured at 22 cm year-1 (Deraniyagala 1939) to 52 cm year-1 (Berkenmeier 1971). Adult growth rates have been measured at 0.25 cm year-1 (Price et al. 2004) thus juvenile growth rates either slowly decline with age or maintain themselves (linearly) until adulthood is reached and then drastically drop off after first nesting episode. Unfortunately, there are no measured growth rates for leatherbacks > 80 cm SCL and < 120 cm SCL.  Growth functions can be used especially when combined with multiple datasets to fill the gaps in our understanding of leatherback growth. We combined our data with other captive studies and adult skeletochronology data for use in the von Bertalanffy, Gompertz and logistic growth functions. Based on the combined length-at-age dataset for leatherback growth the VBGF, Gompertz and logistic growth functions estimated that leatherbacks take 15.3, 6.8 and 3.9 years (Table 5, Figure 3.2A, B, C) to attain mean nesting length, respectively. The estimate from the logistic function is in agreement with the early attainment of sexually mature length from the extrapolation of early growth rates of captive turtles including our growth rates of 31.98 ± 2.8 cm 63  year-1. Figure 3.2C shows that the logistic growth function does predict linear growth until mature nesting lengths are attained. The function does not fit the initial growth data well, however, leading to a high MSres. The logistic function also gives the lowest L∞ value, nearly 2.5 cm less than the VBGF. Furthermore, the logistic growth function gives a Lmin value of 84 cm SCL, to our knowledge and from that of the literature the smallest recorded nesting leatherback is 99 cm SCL (converted from CCL) (Stewart et al. 2007).  The Gompertz growth function estimated age at maturity of 6.8 years which corroborates the estimate of 6 years by Rhodin (1985) and Rhodin et al. (1996) based on the chondro-osseous (cartilage and bone) morphology of leatherbacks. However, the Gompertz growth function like the logistic failed to fit the initial data well (high MSres) and predicts linear growth from 20 cm SCL to nearly 120 cm SCL. The value for L∞ is slightly more than that obtained from the logistic function at 140.8 cm SCL and the value for Lmin is 110 cm SCL. The value for Lmin is within recorded measurements of nesting females but still near the smallest recorded (Stewart et al. 2007). The studies by Rhodin (1985) and Rhodin et al. (1996) suggest rapid growth in leatherbacks but by no means is chondro-osseous morphology an aging or growth rate technique.  The VBGF estimated 15.3 years for age at maturity, the highest of the three growth functions. The VBGF also gave the lowest MSres and the highest L∞ (142.7 cm SCL) and Lmin (121 cm SCL) estimates, which corroborate with a recent study by Stewart et al. (2007) suggesting a mean nesting length for leatherback populations worldwide (Atlantic, Pacific and Indian Oceans) of 147 cm SCL and an average minimum nesting length worldwide of 124 cm SCL (both SCL measurements converted from CCL using eq.1). Our VBGF estimates our similar to that of Zug  64  & Parham (1996), k equals 0.226 and 0.286, respectively and our age-at-maturity is slightly older (15.3 and 13.3 years, respectively). Furthermore, age-at-maturity estimates of 13-15 years corroborate those of Dutton et al. (2005) who estimated that leatherbacks take 12-14 years to mature, based on large increases in numbers of nesting females after intensive conservation efforts produced an order of magnitude increase in hatchling production on St. Croix, USVI. A long term DNA finger-printing study of the same turtles suggested that recent first time nesters were the genetic offspring of turtles sampled in the 1980s, again suggesting age-at-maturity estimates < 20 years (Dutton et al. 2005). In light of (i) the MSres, L∞ and Lmin estimates, (ii) the corroboration of skeletochronology (Zug and Parham 1996), conservation based and genetic (Dutton et al. 2005) aging studies, and (iii) the unlikelihood of linear growth from hatching to 120 cm SCL we suggest the VBGF (Figure 3.2A) gives the more biologically relevant age at maturity estimate of 15.3 years.  Our data suggest that leatherbacks mature at a younger age (15.3 years) than cheloniid turtles and they do so at a longer length. For example, loggerheads and greens take 15 to 40 years to reach a sexually mature length of about 90 and ~ 100 cm SCL, respectively, with green turtles spending nearly 20 years as juveniles (Limpus & Walter 1980, Mendoca 1981, Frazer & Ehrhart 1985, Frazer & Ladner 1986, Bjorndal & Bolten 1988, Seminoff et al. 2002). This suggests that while the early reports of Rhodin (1985) and Rhodin et al. (1996) of 3-6 years for maturity in leatherbacks are probably incorrect, their suggestion that leatherbacks have fast growth rates is correct. Indeed, leatherbacks attain substantially longer mean nesting lengths (147 cm SCL, Stewart et al. 2007) in considerably less time than any other marine turtle.  65  Unlike leatherbacks, most hard shelled turtles experience marked habitat shifts through ontogeny and concomitant changes in diet (e.g., Bjorndal & Bolten 1988). Such transitions are probably also reflected in different growth patterns during those life stages. Therefore, their somatic growth is most likely polyphasic, breaking the assumptions of the VBGF (monotonic decay, noninflexion) and probably better reflected by the sigmoidal Gompertz and logistic growth functions. Leatherbacks, however, are oceanic-pelagic animals throughout their life-history (Bolten 2003) and have no known diet shifts after hatching. Their diet consists solely of gelatinous zooplankton (Bjorndal 1997, Salmon et al. 2004). While several tracking studies have now shown that leatherbacks use neritic waters for foraging (Eckert 2006, Eckert et al. 2006, James et al. 2007, Witt et al. 2007), it is probable that they remain water column feeders (epipelagic) nevertheless (Godley et al. 2008). This further justifies applying the monotonic VBGF to leatherback growth data.  Data on leatherback bycatch in longline and drift gillnet (DGN) fisheries in the Pacific Ocean (Table 3.6) show a dichotomy in the length range of the animals caught by the two types of fishery. These differences probably reflect different leatherback length class assemblages in the zones in which the fisheries operate, with smaller turtles typically being caught in warmer waters. All leatherbacks < 90 cm SCL (mean 78 cm SCL; Table 3.6) were caught in warmer equatorial waters off American Samoa and Hawaii (21.7-28.9 ºC; NDOC 2008). Conversely, data for the DGN fishery in the cooler waters off California and Oregon (5.6-20 ºC; NDOC 2008) report only adult leatherbacks as bycatch (mean 146 cm SCL; Table 3.6). The artisanal fisheries of Peru, operating in relatively stable sea temperatures (16-20.5 ºC; Coker 1918) caused by the Humboldt Current, entangle sub-adult leatherbacks (mean 113 cm SCL; Table 3.6). The  66  trend of larger leatherbacks being caught in colder waters is corroborated by recent studies in the North Atlantic showing a latitudinal gradient in body size with smaller turtles excluded from higher latitude foraging grounds (James et al. 2007 and Witt et al. 2007). Furthermore, Eckert (2002) showed that leatherbacks do not move above ~30 ºN until they are over 100 cm SCL. The difference in SCL with water temperature is probably a reflection of the thermoregulatory capabilities of larger leatherbacks allowing them to exploit colder, more productive foraging grounds (Bostrom and Jones 2007). Given our VBGF and length-mass relationship a 100 cm SCL corresponds to an age of 5.2 years and a mass of 112 kg. This may be the size at which leatherbacks‘ thermoregulatory capabilities allow them to move into colder waters where they can exploit different assemblages and perhaps greater abundance of gelatinous zooplankton.  Interestingly, the smallest leatherbacks interacting with fisheries are ~ 70 cm SCL. This may represent the length at which leatherbacks are large enough to entangle, foul hook or ingest baits in the fishing gear. Alternatively, this may represent the life-history stage at which leatherbacks are congregating in fishery zones where greater abundances of gelatinous prey may be found. While the reason for the increasing number of interactions after 70 cm SCL (2.8 years of age based on our VBGF) remains unknown, it places a heavy human induced mortality on leatherbacks early into their development. With leatherbacks taking 15.3 years (Figure 3.2, Table 3.5) to reach mean nesting length, > 80% of their adolescent and their entire adult life finds them in jeopardy of fishery induced mortality. All studies on early growth in leatherbacks suggest that 70 cm SCL is attained rapidly, i.e. within 3 years, therefore regardless of what growth function or age estimate is used, leatherbacks spend the majority of their lives at risk of marine fishery interactions. While attaining a large mass quickly decreases natural mortality from predation, it  67  unfortunately puts leatherbacks at a greater risk of fishery interactions from an early (adolescent) life history stage.  Decades of intense egg harvesting and widespread incidental bycatch from fisheries have led to drastic declines in leatherback populations, especially in the Pacific Ocean (Eckert & Sarti 1997, Lewison et al. 2004, Alfaro-Shigueto et al. 2007, Sarti Martinez et al. 2007). Although our data indicate that turtles are at risk of being caught as bycatch in fisheries for most of their life, our results also suggest that their growth rate, allowing them to reach mean nesting length after only 15.3 years, offers potential for relatively rapid population recovery, as indicated by Dutton et al. (2005), if fishing moratoria are implemented and enforced for those fisheries that represent the greatest threat.  68  Table 3.1 Weekly measurements of SCL and mass of 20 leatherback turtles raised in captivity. n = number of turtles measured at corresponding age. n decreases with age due to mortality, but some weeks have smaller sample sizes due to turtles being used in other experiments. * Final SCL and mass at death of last remaining turtle, this data pair was not used in study as turtle was on antibiotics and had not eaten for many days.  Age (years) SCL (cm) 0.003 6.31 0.01 6.53 0.03 7.22 0.05 7.81 0.07 8.31 0.08 8.91 0.10 9.59 0.12 10.28 0.14 10.76 0.16 11.33 0.18 12.10 0.20 12.77 0.22 13.20 0.24 13.64 0.26 14.35 0.28 14.92 0.30 15.49 0.32 16.04 0.33 16.49 0.35 17.50 0.37 18.23 0.39 18.88 0.41 19.41 0.43 20.10 0.45 20.55 0.47 21.19 0.49 22.04 0.51 22.61 0.53 23.68 0.55 24.40 0.56 25.14 0.58 25.82 0.60 26.27 0.62 27.57 0.64 28.10 0.66 28.61 0.68 29.31 0.70 30.21 0.72 30.92 0.74 31.74 0.76 32.29 0.78 32.89 0.79 33.36 0.81 33.86  ± sd 0.13 0.18 0.21 0.34 0.36 0.30 0.38 0.42 0.41 0.43 0.46 0.53 0.60 0.63 0.50 0.63 0.77 0.91 1.12 0.73 0.58 0.66 0.67 0.81 0.87 1.01 1.09 1.19 0.92 0.98 0.93 1.17 1.22 0.67 0.56 0.57 0.60 0.48 0.45 0.53 0.49 0.53 0.61 0.67  Mass (kg) 0.046 0.05 0.06 0.08 0.10 0.12 0.15 0.18 0.21 0.24 0.28 0.32 0.37 0.41 0.45 0.51 0.56 0.62 0.68 0.82 0.91 1.01 1.10 1.21 1.28 1.39 1.51 1.68 1.87 2.03 2.28 2.40 2.63 2.81 3.03 3.20 3.47 3.77 4.15 4.30 4.51 4.85 5.10 5.28  ± sd 0.001 0.002 0.003 0.005 0.01 0.01 0.01 0.02 0.02 0.02 0.03 0.03 0.04 0.05 0.04 0.06 0.07 0.09 0.12 0.08 0.08 0.09 0.10 0.12 0.13 0.15 0.18 0.21 0.16 0.17 0.17 0.22 0.25 0.15 0.15 0.18 0.19 0.28 0.26 0.28 0.30 0.43 0.56 0.55  n 20 19 20 20 20 20 20 20 20 20 16 19 18 18 17 16 16 16 16 12 11 11 11 11 10 10 10 10 9 9 9 9 9 7 7 7 7 6 6 6 6 6 6 6  Age (years) SCL (cm) 0.83 34.30 0.85 35.21 0.87 35.36 0.89 36.21 0.91 36.81 0.93 38.26 0.95 39.06 0.97 39.75 0.99 40.44 1.01 41.28 1.02 42.61 1.04 42.53 1.06 43.21 1.08 43.75 1.10 44.97 1.12 45.46 1.14 46.02 1.16 46.08 1.18 46.34 1.22 47.36 1.24 47.57 1.25 47.86 1.31 50.30 1.33 52.00 1.35 53.10 1.37 53.90 1.39 52.60 1.45 54.50 1.47 55.45 1.49 56.30 1.51 56.10 1.53 56.70 1.55 57.10 1.56 57.70 1.59 58.20 1.60 58.70 1.62 59.50 1.67 61.50 1.69 60.20 1.72 62.50 1.85 67.00 1.93 69.00 2.23* 72.00  ± sd 0.65 0.90 0.94 1.12 1.11 0.78 0.93 0.97 1.00 1.28 1.47 1.18 1.00 0.76 1.59 1.68 1.98 1.72 1.95 2.08 2.00 2.43 2.63 2.26 2.26 2.12 -  Mass (kg) 5.49 5.92 6.15 6.61 6.75 7.55 8.00 8.40 8.76 9.12 9.68 9.89 10.57 11.30 11.53 11.63 12.26 11.89 12.05 12.87 12.79 13.23 15.44 17.26 17.99 18.68 17.28 18.76 18.72 19.88 20.20 20.02 20.28 21.14 23.06 22.92 23.56 25.60 25.38 27.20 31.96 34.84 42.65  ± sd 0.63 0.74 0.75 0.87 0.87 0.70 0.66 0.79 0.78 1.00 1.21 1.18 1.29 1.42 1.52 1.62 1.97 1.57 1.82 2.22 2.09 2.05 2.94 2.46 3.01 2.38 -  n 5 5 5 5 5 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 3 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1  69  Table 3.2 SCL and mass-at-age of individual turtles raised in captivity. These data were used in figures 3.2 & 3.3 to produce the length-mass relationships (when mass given) and the growth functions (length-at-age data). turtle id Age (years) SCL (cm) Mass (kg) Source turtle id Age (years) SCL (cm) Mass (kg) 1 0.20 13.29 0.34 this study 2 0.12 6.60 2 0.43 20.69 1.28 this study 3 0.12 7.80 3 1.25 46.45 11.54 this study 4 0.13 7.00 4 0.60 24.65 2.36 this study 5 0.16 7.80 5 1.31 48.40 13.04 this study 6 0.21 8.00 6 0.60 24.06 2.20 this study 7 0.22 8.10 7 1.93 69.00 34.84 this study 8 3.70 85.00 49.50 8 0.33 15.01 0.58 this study 9 0.12 8.10 9 0.68 28.38 3.42 this study 10 0.17 7.90 10 0.26 14.62 0.46 this study 11 0.30 8.90 11 0.18 11.57 0.26 this study 12 0.41 10.40 12 1.37 55.40 20.36 this study 13 0.41 10.80 13 0.81 34.74 5.44 this study 14 0.50 14.30 14 0.24 12.05 0.30 this study 0.22 13.20 15 0.35 16.00 0.70 this study 0.67 21.6 1.04 16 0.33 17.15 0.75 this study 1 0.56 37.00 4.50 17 0.91 35.13 5.58 this study 2 0.27 17.00 18 0.33 13.69 0.37 this study 3 0.23 14.00 19 0.33 14.95 0.46 this study 4 0.19 12.00 20 0.51 19.87 1.20 this study 5 0.13 10.50 A 1.71 43.50 7.27 Deraniyagala, 1939 6 0.12 12.50 C 0.46 16.00 Deraniyagala, 1939 7 0.13 12.50 1 0.08 5.40 Bels et al., 1988 8 0.13 12.00 -  Source Bels et al., 1988 Bels et al., 1988 Bels et al., 1988 Bels et al., 1988 Bels et al., 1988 Bels et al., 1988 Bels et al., 1988 Bels et al., 1988 Bels et al., 1988 Bels et al., 1988 Bels et al., 1988 Bels et al., 1988 Bels et al., 1988 Frayr, 1970 Phillips, 1977 Birkenmeier, 1970 Birkenmeier, 1970 Birkenmeier, 1970 Birkenmeier, 1970 Birkenmeier, 1970 Birkenmeier, 1970 Birkenmeier, 1970 Birkenmeier, 1970  70  Table 3.3 SCL and mass of 11 wild individual turtles (stranded or as bycatch). Date, location, and source are given for each turtle, except one, for which only the SCL and mass are known. These data are used in figures 3.1 & 3.2.  Date Location SCL (cm) Mass (kg) Source Aug-93 American Samoa 39 7.00 MTN (1994; no. 66, p. 3-5) Sep-34 Ceylon, India 8.5 0.096 Deraniyagala (1939; Ceylon Journal of Science) J. Wyneken (pers. comm. ) Sep-05 Florida, USA 10.4 0.19 J. Wyneken (pers. comm. ) Mar-06 Florida, USA 25 3.10 Apr-98 Hawaii, USA 70.4 44.50 NOAA (NMFS/PIFSC) Apr-99 Hawaii, USA 85.3 74.10 NOAA (NMFS/PIFSC) Apr-06 Hawaii, USA 70 35.45 NOAA (NMFS/PIFSC) Jul-06 Hawaii, USA 67.5 33.60 NOAA (NMFS/PIFSC) Jul-02 W. Australia 20 1.85 MTN (2004; no. 104, p. 3-5) 1983 W. Australia 31 3.30 MTN (2004; no. 104, p. 3-5) M. Conti (pers. comm. ) Unknown Unknown 11.5 0.17  71  Table 3.4 Growth rate (ΔL/Δt) and mid-length (L1 + L2)/2 calculated from leatherback turtle growth data (Table 1) separated into 10 cm SCL data bins. For each data group (length bin) the standard deviations (sd) and total number of measurements (n) are given. * indicates a significant drop in growth rate. (Tukey Kramer post-hoc, p<0.05).  Length bin < 10 cm SCL 10 to 20 cm SCL 20 to 30 cm SCL 30 to 40 cm SCL 40 to 50 cm SCL 50 to 60 cm SCL 60 to 70 cm SCL  Avg mid-lengths (cm SCL) 7.95 14.36 24.65 34.32 44.56 54.67 64.42  ± sd 1.09 2.85 2.88 2.74 2.49 2.68 3.76  Growth rate (cm year-1) 33.28 29.62* 34.81 34.21 26.77* 31.68 32.90  ± sd 12.65 11.00 10.53 13.87 19.45 17.30 6.28  n 130 259 115 77 51 19 3  72  Table 3.5 Parameters from three growth models (von Bertalanffy, Gompertz, and logistic) for the length-at-age data in Table 3.2 and adult data from skeletochronology studies (Zug & Parham 1996). Parameters include the asymptotic length in cm SCL (L∞), intrinsic growth rate year-1 (k), predicted age in years at length = 0 (t0), adjusted regression coefficient (R2), residual mean squares (MSres), predicted age-at-maturity in years (age), and minimum length in cm SCL at maturity as given by the 95% prediction band (Lmin). Standard error (se).  Growth model  L∞  ± se  VBGF Gompertz Logistic  142.7 140.8 140.4  1.64 1.56 1.63  k  ± se  0.2262 0.021 0.511 0.201 0.883 0.121  2  t0  ± se  R  -0.17 1.86 2.6  0.07 0.14 0.2  0.98 0.98 0.97  MSres  age  Lmin  81.73 93.42 103.71  15.3 6.8 3.9  121 110 84  73  Table 3.6 SCL of leatherback turtles bycaught in artisanal, drift gill net (DGN) and longline fisheries of the Pacific Ocean. CA-OR is used as abbreviation for the DGN fishery off California and Oregon, USA. This list of leatherbacks represents only those animals that were measured and not the total number of bycatch incidents.  SCL (cm) 39 50 64.5 67.5 70 70.4 71 80 85.3 87.5 130 98-123 111-165 113-160 132 136 155.5 160  Location / Fishery American Samoa / Longline Hawaii / Longline Observer data Hawaii / Longline Observer data Hawaii / Longline Observer data Hawaii / Longline Observer data Hawaii / Longline Hawaii / Longline Observer data Hawaii / Longline Observer data Hawaii / Longline Hawaii / Longline Observer data Hawaii / Longline Observer data Peru / Artisanal Peru / unknown Gulf of California / Longline CA-OR / DGN CA-OR / DGN CA-OR / DGN CA-OR / DGN  Source MTN (1994; no 66, p. 3-5) NOAA (NMFS/PIFSC) NOAA (NMFS/PIFSC) NOAA (NMFS/PIFSC) NOAA (NMFS/PIFSC) Fish. Bull. (2002; no 100, p. 876-880) NOAA (NMFS/PIFSC) NOAA (NMFS/PIFSC) Fish. Bull. (2002; no 100, p. 876-880) NOAA (NMFS/PIFSC) NOAA (NMFS/PIFSC) CCB (2007; no 6, p. 129-134) CCB (2007; no 6, p. 129-134) CCB (2007; no 6, p. 137-141) NOAA (NMFS/SWFSC) NOAA (NMFS/SWFSC) NOAA (NMFS/SWFSC) NOAA (NMFS/SWFSC)  mean 78 113 136 139.4 146  Hawaii / Longline Peru / Artisanal Peru / unknown Gulf of California / Longline CA-OR / DGN  NOAA (NMFS/PIFSC) CCB (2007; no 6, p. 129-134) CCB (2007; no 6, p. 129-134) CCB (2007; no 6, p. 137-141) NOAA (NMFS/SWFSC)  74  A  log body mass (kg)  2  1  0  this study  -1 1.0  1.2  1.4  1.6  1.8  2.0  log straight carapace length (cm) Figure 3.1 Log of length-mass relationships (log length-log mass) of leatherback turtles for (A) this study (Table 3.2). The relationship for A is log(y)=-3.54+2.78log(x). The solid black line denotes the best-fit line (as given in the previous equations), the inner blue lines are the 95% confidence bands around the regression and the outer red lines are the 95% prediction bands for the scatter of data pairs.  75  log body mass (kg)  3  B  2  1  0  -1 wild juveniles  -2 0.8  1.0  1.2  1.4  1.6  1.8  2.0  log straight carapace length (cm) Figure 3.1 Log of length-mass relationships (log length-log mass) of leatherback turtles for (B) juveniles from the wild (Table 3.3). The relationship for B is log(y)=-3.63+2.84log(x). The solid black line denotes the best-fit line (as given in the previous equations), the inner blue lines are the 95% confidence bands around the regression and the outer red lines are the 95% prediction bands for the scatter of data pairs.  76  4  C  log body mass (kg)  3 2 this study Deraniyagala 1939 Bels et al. 1988 Phillips 1977 Berkenmeier 1970 wild juveniles, table 3 wild adults: Deraniyagala 1939, Eckert et al. 1989, Boulon et al. 1996, James et al. 2005, Georges & Fossette 2006, B. Wallace pers. comm.  1 0 -1 -2 0.8  1.0  1.2  1.4  1.6  1.8  2.0  2.2  2.4  log straight carapace length (cm) Figure 3.1 Log of length-mass relationships (log length-log mass) of leatherback turtles for (C) all available L-M data pairs. The relationship for C is log(y)=-3.67+2.86log(x). The solid black line denotes the best-fit line (as given in the previous equations), the inner blue lines are the 95% confidence bands around the regression and the outer red lines are the 95% prediction bands for the scatter of data pairs.  77  A 160 140  cm SCL  120 100  All data 95% Confidence Band 95% Prediction Band this study Deraniyagala 1939 Berkenmeier 1970 Frayr 1970 Phillips 1977 Bels et al. 1988 Zug and Parham 1996  80 60 40 20 0 0  5  10  15  20  25  30  Age (years) Figure 3.2 The (A) von Bertalanffy growth functions (VBGF) for leatherback turtles: Black line: best-fit curve using nonlinear least squares (NLLS) regression with a fitted value of L∞ = 142.7 cm, k = 0.226 year-1and t0 = -0.17 year. Blue line: 95% confidence bands around the regression. Red line: 95% prediction bands for the scatter of data pairs.  78  B 160 140  cm SCL  120 100  All data 95% Confidence Band 95% Prediction Band this study Deraniyagala 1939 Berkenmeier 1970 Frayr 1970 Phillips 1977 Bels et al. 1988 Zug and Parham 1996  80 60 40 20 0 0  5  10  15  20  25  30  Age (years) Figure 3.2 The (B) Gompertz growth function for leatherback turtles: Black line: best-fit curve using nonlinear least squares (NLLS) regression with a fitted value of L∞ = 140.8 cm, k = 0.511 year-1and t0 = 1.86 year. Blue line: 95% confidence bands around the regression. Red line: 95% prediction bands for the scatter of data pairs.  79  160  C  140  cm SCL  120 100  All data 95% Confidence Band 95% Prediction Band this study Deraniyagala 1939 Berkenmeier 1970 Frayr 1970 Phillips 1977 Bels et al. 1988 Zug and Parham 1996  80 60 40 20 0 0  5  10  15  20  25  30  Age (years) Figure 3.2 The (C) logistic growth function for leatherback turtles: Black line: best-fit curve using nonlinear least squares (NLLS) regression with a fitted value of L∞ = 140.4 cm, k = 0.883 year-1and t0 = 2.6 year. Blue line: 95% confidence bands around the regression. Red line: 95% prediction bands for the scatter of data pairs.  80  REFERENCES Alfaro-Shigueto J, Dutton PH, Van Bressman M-F, Mangel J (2007) Interactions between leatherback turtles and Peruvian artisanal fisheries. Chelonian Conservation and Biology 6(1): 129-134 Balazs GH, Chaloupka M (2004) Spatial and temporal variability in somatic growth of green sea turtles (Chelonia mydas) resident in the Hawaiian Archipelago. Marine Biology 145: 10431059 Bels V, Rimblot-Baly F, Lescure J (1988) Croissance et maintien en captivité de la tortue luth Dermochelys coriacea (Vandelli, 1761). Revue Francaise d'Aquariologie 15(2): 59-64 Birkenmeier E (1971) Juvenile leatherback turtles, Dermochelys coriacea (Linnaeus), in captivity. Museum Journal 3(1): 160-172 Bjorndal KA, Bolten AB (1988) Growth rates of immature green turtles, Chelonia mydas, on feeding grounds in the southern bahamas. Copeia 3: 555-564 Bjorndal KA (1997) Foraging ecology and nutrition of sea turtles. In: Lutz P, Musick J (eds) The Biology of Sea Turtles. CRC Press, Boca Raton. pp. 199-232 Bolten AB (2003) Variation in sea turtle life history patterns: neritic vs. oceanic development stages. In: Lutz PL, Musick JA, Wyneken J (eds) The Biology of Sea Turtles, Book 2. CRC Press, Boca Raton. pp. 243-258 Bostrom BL, Jones DR (2007) Exercise warms adult leatherback turtles. Comparative Physiology Biochemistry Part A 147: 323–331 Boulon RH, Dutton PH, McDonald DL (1996) Leatherback turtles Dermochelys coriacea on St. Croix, U.S. Virgin Islands: fifteen years of conservation. Chelonian Conservation and Biology 2: 141-147 81  Buskirk JV, Crowder LB (1994) Life-history variation in marine turtles. Copeia 1994: 66-81 Cailliet GM, Mollet HF, Pittenger GG, Bedford D, Natanson LJ (1992) Growth and demography of the Pacific angel shark (Squatina californica), based upon tag returns of California. Australian Journal of Marine and Freshwater Research 43: 1313-1330 Chaloupka MY, Musick JA (1997) Age, growth, and population dynamics. In: Lutz P, Musick J (eds) The Biology of Sea Turtles. CRC Press, Boca Raton. pp. 233-276 Chan EH (1988) A note on the feeding of leatherback Dermochelys coriacea hatchlings. Pertanika 11(1): 147-149 Coker RE (1918) Ocean temperatures off the coast of Peru. Geographical Review 5(2): 127-135 Deraniyagala PEP (1939) The Tetrapod Reptiles of Ceylon Volume I. Testudinates and Crocodilians. Ceylon Journal of Science. Sunil Printers, New Delhi Dutton DL, Dutton PH, Chaloupka M, Boulon RH (2005) Increase of a Caribbean leatherback turtle Dermochelys coriacea nesting population linked to long-term nest protection. Biological Conservation 126: 186-194 Eckert SA, Eckert KL, Ponganis P, Kooyman GL (1989) Diving and foraging behavior of leatherback sea turtles (Dermochelys coriacea). Canadian Journal of Zoologists 67: 28342840 Eckert SA, Sarti LM (1997) Distant fisheries implicated in the loss of the world's largest leatherback nesting population. Marine Turtle Newsletter 78: 2-7 Eckert SA (2002) Global distribution of juvenile leatherback turtles, Dermochelys coriacea. Marine Ecology Progress Series 230: 289-293 Eckert SA (2006) High-use oceanic areas for Atlantic leatherback sea turtles. Marine Biology 149: 1257-1267  82  Eckert SA, Bagley D, Kubis S, Ehrhart L, Johnson C, Stewart K, DeFresse D (2006) Internesting and postnesting movements and foraging habitats of leatherback sea turtles (Dermochelys coriacea) nesting in Florida. Chelonian Conservation and Biology 5: 239–248 Fabens AJ (1965) Properties and fitting of the von Bertalanffy growth curve. Growth 29: 265289 Foster P, Chapman C (1975) The care and maintenance of young leatherback turtles, Dermochelys coriacea. International Zoo Yearbook 15: 170-171 Frayr W (1970) The world's largest living turtle. Salt Water Aquarium 5: 235-241 Frazer NB, Ehrhart LM (1985) Preliminary growth models for green, Chelonia mydas, and loggerhead, Caretta caretta, turtles in the wild. Copeia 1985: 73-79 Frazer NB, Ladner RC (1986) A growth curve for green sea turtles, Chelonia Mydas, in the U.S. Virgin Islands, 1913-1914. Copeia 1986(3): 798-802 Grant GS (1994) Juvenile leatherback turtle caught by longline fishing in American Samoa. Marine Turtle Newsletter 66: 3-5 Godley BJ, Blumenthal JM, Broderick AC, Coyne MS, Godfrey MH, Hawkes LA, Witt MJ (2008) Satellite tracking of sea turtles: Where have we been and where do we go next? Endangered Species Research 4: 3-22 Georges JY, Fossette S (2006) Estimating body mass in leatherback turtles Dermochelys coriacea. Marine Ecology Progress Series 318: 255-262 Hayes JP, Shonkwiler JS (2001) Morphological indicators of body condition: worthwhile or wishful thinking? In: Speakman JR (ed) Body Composition of Animals: a Handbook of NonDestructive Methods. Cambridge University Press, Cambridge. pp. 8-38  83  Heppell SS, Crowder LB, Crouse DT, Epperly SP, Fraser NB (2003) Population models for Atlantic loggerheads: past, present, and future. In Bolten AB, Witherington BE (eds) Loggerhead Sea Turtles. Smithsonian Books, Washington DC, p 255-273 IUCN (2007) 2007 IUCN Red List of Threatened Species. Accessed 2 March 2008. www.iucnredlist.org James MC, Ottensmeyer CA, Myers RA (2005) Identification of high-use habitat and threats to leatherback sea turtles in northern waters: new directions for conservation. Ecology Letters 8: 195-201 James MC, Sherill-Mix SA, Myers RA (2007) Population characteristics and seasonal migrations of leatherback sea turtles at high latitudes. Marine Ecology Progress Series 337: 245-254 Jones TT, Salmon M, Wyneken J, Johnson C (2000) Rearing leatherback hatchlings: protocols, growth and survival. Marine Turtle Newsletter 2000 (90): 3-6 Lewison RL, Freeman SA, Crowder LB (2004) Quantifying the effects of fisheries on threatened species: the impact of pelagic longlines on loggerhead and leatherback sea turtles. Ecology Letters 7(3): 221-231 Limpus C, Walter DG (1980) The growth of immature green turtles (Chelonia mydas) under natural conditions. Herpetologica 36: 162-165 Mendoca MT (1981) Comparative growth rates of wild immature Chelonia mydas and Caretta caretta in Florida. Journal of Herpetology 15(4): 447-451 NDOC (2008) National Oceanographic Data Center. Accessed on 16 March 2008. www.nodc.noaa.gov/dsdt/cwtg/npac.html Motulsky H, Christopolous A (2004) Fitting models to biological data using linear and nonlinear regression: A practical guide to curve fitting. Oxford University Press, Inc. New York  84  Phillips EJ (1977) Raising hatchlings of the leatherback turtle, Dermochelys coriacea. British Journal of Herpetology 5: 677-678 Price ER, Wallace BP, Reina RD, Spotila JR, Paladino FV, Piedra R, Velez E (2004) Size, growth, and reproductive output of adult female leatherback turtles Dermochelys coriacea. Endangered Species Research 5: 1-8 Prince RIT (2004) Stranding of small juvenile leatherback turtle in western Australia. Marine Turtle Newsletter 104: 3-5 Rhodin AGJ (1985) Comparative chondro-osseous development and growth of marine turtles. Copeia 1985: 752-771 Rhodin JAG, Rhodin AGJ, Spotila JR (1996) Electron microscopic analysis of vascular cartilage canals in the humeral epiphysis of hatchling leatherback turtles, Dermochelys coriacea. Chelonian Conservation and Biology 2(2): 250–260 Ricklefs RE (1967) A graphical method of fitting equations to growth curves. Ecology 48(6): 978-983 Salmon M, Jones TT, Horch K (2004) Ontogeny of diving and feeding behavior in juvenile sea turtles: A comparison study of green turtles (Chelonia mydas L.) and leatherbacks (Dermochelys coriacea L.) in the Florida current. Journal of Herpetology 38: 36-43 Sarti-Martinez L, Barragan AR, Munoz DG, Garcia N, Huerta P, Vargas F (2007) Conservation and biology of the leatherback turtle in the Mexican Pacific. Chelonian Conservation and Biology 6(1): 70–78 Seminoff JA, Resendiz AR, Nichols WJ, Jones TT (2002) Growth rates of wild green turtles (Chelonia mydas) at a temperate foraging area in the Gulf of California, Mexico. Copeia 2002(3): 610-617  85  Seminoff JA, Dutton PH (2007) Leatherback turtles (Dermochelys coriacea) in the Gulf of California: distribution, demography, and human interactions. Chelonian Conservation and Biology 6: 137-141 Spoczynska JOI (1970) Rearing hatchlings of Dermochelys coriacea L. British Journal of Herpetology 4: 189-192 Spotila JR, Dunham AE, Leslie AJ, Steyermark AC, Plotkin PT Paladino FV (1996) Worldwide population decline of Dermochelys coriacea: are leatherback turtles going extinct? Chelonian Conservation and Biology 2: 209-222 Spotila JR, Reina RD, Steyermark AC, Paladino FV (2000) Pacific leatherback turtles face extinction. Nature 405: 529-530 Stewart K, Johnson C, Godfrey MH (2007) The minimum size of leatherbacks at reproductive maturity, with a review of sizes for nesting females from the Indian, Atlantic, and Pacific Ocean basins. Herpetological Journal 17: 123-128 Tucker AD, Frazer NB (1991) Reproductive variation in leatherback turtles, Dermochelys coriacea, at Culebra National Wildlife Refuge, Puerto Rico. Herpetologica 47: 115–124 von Bertalanffy L (1938) A quantitative theory of organic growth (Inquiries on growth laws. II.). Human Biology 10(2): 181-213 Wabnitz C, Pauly D (2008) Length–weight relationships and additional growth parameters for sea turtles. In: Lourdes M, Palomares D, Pauly D (eds) von Bertalanffy Growth Parameters of Non-Fish Marine Organisms. Fisheries Center Research Reports 16(10): 137 Witham R (1977) Dermochelys coriacea in captivity. Marine Turtle Newsletter 3: 6  86  Witt MJ, Broderick AC, Johns DJ, Martin CS, Penrose R, Hoogmoed MS, Godley BJ (2007) Prey landscapes help identify potential foraging habitats for leatherback turtles in the northeast Atlantic. Marine Ecology Progress Series 337: 231–244 Witzell WN (1980) Growth of captive hawksbill turtles, Eretmochelys imbricate, in western Samoa. Bulletin of Marine Science 30(4): 909-912 Work TM, Balazs GH (2002) Necropsy findings in sea turtles taken as bycatch in the North Pacific longline fishery. Fisheries Bulletin 100: 876-880 Zar JH (1999) Biostatistical Analysis 4th edn. Prentice Hall, New Jersey Zug GR, Parham JF (1996) Age and growth in leatherback turtles, Dermochelys coriacea (Testudines: Dermochelyidae): a skeletochronological analysis. Chelonian Conservation and Biology 2(2): 244-249 Zug GR, Balazs GH, Wetherall JA, Parker DM, Murakawa SKK (2002) Age and growth of Hawaiian green sea turtles Chelonia mydas: an analysis based on skeletochronology. Fisheries Bulletin 100: 117-127  87  CHAPTER FOUR: POPULATION STATUS AND TOTAL BIOMASS OF PACIFIC LEATHERBACK TURTLES (DERMOCHELYS CORIACEA) DERIVED FROM GROWTH AND FOOD CONVERSION STUDIES IN CAPTIVE LEATHERBACKS3 INTRODUCTION Leatherback turtles (Dermochelys coriacea) have the largest range of any marine turtle, spanning sub-polar waters to tropical beaches (Goff and Lien 1988, Steyermark et al. 1996); are capable of thermoregulation (Paladino et al. 1990, Bostrom and Jones 2007); migrate across entire ocean basins (Hays et al. 2004, Benson et al. 2007); are the largest extant turtle as well as one of the fastest growing (Eckert and Luginbuhl 1988, Zug and Parham 1996); and subsist solely on an energy-poor diet of gelatinous zooplankton (Bleakney 1965, Brongersma 1969, Hartog and van Nierop 1984). Leatherbacks are listed as critically endangered (IUCN 2008) and are at risk of extirpation or even extinction in the Pacific Ocean (Spotila et al. 1996, Spotila et al. 2000). Despite an increase in research efforts over the last decade there still remains a lack of basic physiological and ecological data on growth rates, age-at-maturity, and daily energy requirements of leatherback turtles.  Food requirement is perhaps the most useful measure for understanding the constraints on the bioenergetics of leatherbacks, because it represents the energy that has to be derived from resources available in the animal‘s habitat (Nagy 1989). Extrapolating individual daily energy  3  A version of this chapter will be submitted in May 2009. Jones TT, Bostrom BL, Hastings MD, Pauly D, Jones DR. 88  demands to an entire population allows an understanding of the dynamics involved in determining animal abundance and distribution (Jones et al. 2004). For instance, a recent study by Wallace et al. (2006) calculated the costs associated with nesting in North Atlantic and eastern Pacific leatherbacks. The authors found that limited resource availability constrained energy allocation to reproduction in eastern Pacific leatherbacks therefore lowering their reproductive output (Wallace et al. 2006). The ability of an animal to allocate energy to activities such as growth and reproduction depends on the animal finding resources above and beyond daily maintenance requirements, as resource allocation is competitive, i.e., increases in allocation to locomotion or growth come at the reduction of energy allocated to other activities (Congdon et al. 1982).  Several studies have started to look at resource availability. For example, Witt et al. 2007 used continuous plankton recorder survey data to map gelatinous zooplankton landscapes in the North Atlantic, while Hawkes et al. 2006 looked at turtle movements in relation to chlorophyll-a, an indication of primary productivity, assuming an association between primary productivity and prey of marine turtles. Further studies have determined how climatic patterns (e.g., El Niño Southern Oscillation) affect the yearly abundance of resources in the eastern Pacific (Saba et al. 2007, 2008a, 2008b). Satellite tracking suggests that leatherbacks do indeed follow jellyfish distributions during their post-nesting migrations (James et al. 2005, Eckert et al. 2006, Hays et al. 2006, Houghton et al. 2006, Benson et al. 2007). However, basic data regarding daily energetic demands are still limited to oxygen consumption data on hatchlings (Lutcavage and Lutz 1986, Wyneken 1997, Jones et al. 2007) or nesting females on beaches (Paladino et al. 1990, Lutcavage et al. 1992, Paladino et al. 1996); although a recent study used doubly labeled  89  water to estimate metabolic rate on internesting females (Wallace et al. 2005; see Wallace and Jones 2008 for review). As a note, Wallace et al. (2005) were only able to obtain metabolic rate on three individuals, due to complications arising from high water flux rates. To our knowledge there is only one report documenting daily food intake of leatherbacks in the wild (DuronDufrenne 1987), and this report was based on observing a leatherback foraging at the surface. Therefore, it seems fair to conclude that existing data leave large gaps in our knowledge of the ontogeny of energy requirements across all life-history stages of leatherback sea turtles.  In this study we describe how we: 1) determined the food intake (daily energy requirements) for individual leatherbacks from growth and food conversion rates (Pauly 1986, Pauly et al. 1993) of our captive stock; 2) determined leatherback population biomass and population food consumption rates by combining measured growth and food intake rates with estimates of mortality (Ricker 1975); 3) determined high and low estimates of food intake, population biomass and population food intake rates by uncertainty analysis; and 4) validated the output of our model with metabolic data from the literature.  MATERIALS & METHODS Animal care Leatherback turtles were obtained from the Virgin Islands, on Canada CITES Import permit CA05CWIM0039 and British Virgin Islands CITES Export certificate CFD062005. These animals were housed and maintained for research purposes and all animal care standards of the Canadian Council for Animal Care (CCAC) and the UBC Animal Care Committee were met (UBC Animal Care Protocol: A04-0323). 90  Twenty hatchlings (emergence July 2nd, 2005) were transported from Tortola, British Virgin Islands (BVI) to the Animal Care Center, Department of Zoology, University of British Columbia. The complete husbandry protocols used in this study are given in Jones et al. (in review). In short, the turtles were maintained in large oval tanks (5 m long x 1.5 m wide x 0.3 m deep) containing ~ 2,500 l of re-circulated/filtered salt water. As the turtles grew in size, header tanks were added that doubled or tripled the active volume of filtered water per turtle. The water temperature was maintained at 24 ± 1 C. Four fluorescent fixtures (40 W UVA/B; Repti-Glow® 8) suspended 0.5 m above each tank provided full spectrum radiation for 12 hours per day; each tank was also exposed to ambient light. Water quality was maintained between the following levels: pH = 8.0-8.3; salinity = 28-33 ppt; and ammonia < 0.1 mg-1.  Diet The diet of wild leatherbacks consists solely of gelatinous zooplankton (e.g., jellyfish, ctenophores). We made a diet that replicated natural diet in terms of texture and that the turtles would accept which could be made readily and consistently with respect to energy and water content throughout the study period. It consisted of squid (Pacific Ocean squid, Todarodes pacificus; mantle and tentacles only), vitamins (ReptaviteTM), and calcium (Rep-CalTM), blended with unflavored gelatin in hot water. The mixture was poured into shallow trays and refrigerated. The solidified diet was cut into strips for ease of feeding and weighing. Turtles were fed 3 to 5 times daily to satiation during the first 2-months of age, and 3 times daily to satiation when > 2months of age. The food for each leatherback was weighed before, and the residue after, feeding to give food intake (Ek-1200 A; Stites Scale Inc., 3424 Beekman Street, Cincinnati, OH 45223).  91  Food samples taken at random from a mixture of several food batches were dried in a desiccating oven at 60 °C for 24 to 72 hours to determine dry to wet weight ratios. Dried homogenized samples were analyzed for energy content by bomb calorimetry (Parr Instrument Co., 211 Fifty Third Street, Moline, Illinois 61265) at the Southwest Fisheries Science Center of the National Oceanic and Atmospheric Administration (NOAA, La Jolla, California, USA). The food had a water content of 90% and an energy content of 20.16 ± 0.39 kJ g-1 dry mass (DM). The former almost matched the water content of jellyfish, which can be upwards of 96% (Doyle et al. 2007), while the latter was 10 times greater than the energy content of common gelatinous prey items of leatherback turtles (Lutcavage and Lutz 1986, Davenport and Balazs 1991, Doyle et al. 2007). Food intake values were converted to energy intake using the gross energy content of the food. Based on energy findings, the total mass of jellyfish consumed by leatherbacks was derived by increasing our gelatin diet by a factor of 10.  Growth The turtles were weighed weekly using an Ek-1200 A scale from hatching to body mass 1.2 kg (± 0.001 kg), and an ADAM CPW-60 scale (Dynamic Scales, 1466 South 8th Street, Terre Haute, IN 47802) for mass > 1.2 kg (± 0.02 kg).  Growth measurements were fitted to the von Bertalanffy growth function (VBGF) (von Bertalanffy 1938),  eq. 1)    Wt  W 1  e k t t0     b  92  where ‗Wt‘ is the predicted mass (kg) at age ‗t‘, ‗W∞‘ is the mass all adults in the population asymptotically approach, ‗k‘ is a growth parameter (not a growth rate) of dimension time-1, ‗t0‘ is the theoretical age at mass = 0, and ‗b‘ is the exponent from a length-mass relationship of the form:  eq. 2)  W  a  Lb  where ‗W‘ is mass in kg, ‗L‘ is length in cm straight carapace length (SCL), ‗a‘ is a multiplicative parameter and ‗b‘ an exponent usually having a value close to 3. We determined ‗W∞‘ and ‗b‘ from the length-mass relationship from Jones et al. (in review) (Chapter 3), with ‗a‘ and ‗b‘ estimated at 2.14x10-4 ± 1.4x10-5 and 2.86 ± 0.01, respectively. The first derivative of the VBGF (dWt/dt) of the form:  eq. 3)  dWt  dt     Wbk 1  e k t t0    e b1  k t t0     represents the growth rate and declines linearly with mass, reaching zero at ‗W∞‘.  Food conversion and consumption Feeding experiments allow calculation of gross food conversion efficiency ‗K1‘ (Ivlev 1945, Pauly 1986, Pauly et al. 1993). It can be calculated by dividing body mass increase over a specified time by the rate of food consumption (‗F1‘), or:  93  eq. 4)  dW K1   t  F1  dt    We used our weekly measurements of mass gain and food consumed by individual leatherbacks to determine values of ‗K1‘. Next we paired ‗K1‘ estimates with the average of the animal‘s mass   Mi  M j over the time increment,  2     , to create x-y data pairs. These values were related to the   mass of the animals by the following function (Pauly 1986, Pauly et al. 1993):  eq. 5)  W K1  1   t  W        where ‗β‘ is a constant. It is a property of the model that ‗K1‘ approaches 0 as ‗Wt‘ approaches ‗W∞‘. Eq. 5 was fitted by linear regression of its log-log transformation:  eq. 6)   log1  K1    logWt    logW   The rate of food consumption as a function of age (F1,t) can be determined by rearranging eq. 4:  eq. 7)  dW F1,t   t  K1,t dt    94  where ‗K1,t‘ is the animal‘s conversion efficiency as a function of age (determined by combining equations 1 & 5). Substituting these equations into eq. 7, food consumption (F1,t) can be plotted as:  eq. 8)        1  e k t t0  b 1 e k t t0  F1,t  W bk   1  1  e k t t0  b           giving the food intake of an animal at any age in its life.  Metabolic rate and validation of food consumption model Energy is consumed by an animal in the form of food and that food energy is either stored or used by the animal in external work or internal heat production (Nagy 1989, Speakman 1997, Sherwood 2005). Growing animals can be considered to be in a positive energy balance, with the amount of energy taken in being greater than that expended. The extra energy is either stored as adipose tissue or glycogen or used in somatic growth. Mature animals are probably in a neutral energy balance where food intake more closely matches the amount of energy expended (Sherwood 2005). The fate of ingested food energy (C) can be expressed by the following equation (Nagy 1989, Speakman 1997):  eq. 9)  C  P  S  R  F  Me U  where ‗P‘ = production (i.e., growth) and ‗S‘ = storage (e.g., glycogen stores in cells) although these terms are commonly listed together simply as ‗P‘, ‗R‘ = respiration (i.e., metabolic rate), 95  ‗F‘ = feces, ‗Me‘ = methane gas produced in the alimentary tract and ‗U‘ = excretion (i.e., nitrogenous waste). The terms ‗P‘, ‗S‘, ‗R‘, and ‗U‘ refer to the apparent absorption (A); apparent because secretions are added to the gut thus ‗A‘ is the net absorbed energy. The efficiency of this process is known as the assimilation efficiency (AE) given as a %.  By manipulation of eq. 9 to solve for ‗R‘, and matching the terms with our data for growth and food intake (F1,t) as gross energy intake, conversion rates (1 – K1), and assimilation efficiency (AE) allows for determination of metabolic rate (MR) as follows:  eq. 10)  MR  F1,t  1  K1  AE  Estimates of food consumption and biomass of the population Estimates of the biomass of the population of leatherbacks & their food consumption can be determined if mortality estimates are known. To this end, we used published data on leatherback nesting ecology in the Pacific Ocean (data on the Atlantic and Indian Oceans are incomplete) to determine number of nesting females per year (Spotila et al. 1996, Dutton et al. 2007, Sarti et al. 2007, Tomillo et al. 2007) and then multiplied this by: nests per female (Reina et al. 2002), eggs per nest (Reina et al. 2002, Tapilatu and Tiwari 2007), and % hatching success (Spotila et al. 1996, Bell et al. 2004, Tapilatu and Tiwari 2007), resulting in an estimate of how many hatchlings enter the Pacific on average each year (Table 4.1). We assumed survivorship during the first year to be 25% of the total number of hatchlings (Spotila et al. 1996). We estimated number of new recruits entering the adult population each year by (i) taking % first time nesters each year (49.5%) (Tomillo et al. 2007) and multiplying this by total number of nesting females 96  per year (1347) (Spotila et al. 1996, Dutton et al. 2007, Sarti et al. 2007, Tomillo et al. 2007); (ii) multiplying this by 2, i.e., assuming a 1:1 male to female sex ratio - time at recruitment was based on age-at-maturity estimates from Jones et al. (in review) (Chapter 3). We used ± 2 standard errors of the mean (SEM) from the averages given (or range if error not given) for nests per year, eggs per nest, etc… to get best and worst case scenarios for mortality estimates. These data were then matched to a mortality equation (Ricker 1975) of the form:  eq. 11)  N t  R  e  Z t  t R   where ‗Nt‘ is the number of individuals living at age ‗t‘, ‗R‘ is the number of recruits, ‗Z‘ is the instantaneous mortality rate where ‗  ln 2 ‘ gives the half-life (i.e. time when there will be half the Z  recruitment number of turtles), and ‗tR‘ refers to the age at recruitment or in our case hatching (tR = 0), yearlings (tR = 1), and age-at-maturity (tR = 15.3) (Jones et al. in review, Chapter 3). Annual mortality rate (A) can be determined from eq. 9 by allowing ‗ A  1  e  Z ‘.  Multiplying food consumption as a function of age (eq. 8) with mortality estimates (eq. 11) gives food intake per year for the estimated population size:  eq. 12)  Q  F1,t  Nt  97  where ‗ Q  ‘ represents the intake in tonnes per year of jellyfish. The biomass of a population ‗ B  ‘ in tonnes can be determined by multiplying increase in mass (eq. 1) with mortality estimates (eq. 11):  eq. 13)  B  Wt  N t  Finally, by dividing the integration of ‗ Q  ‘ (eq. 10) by the integration of ‗ B  ‘ (eq. 13) we obtain the quantity of jellyfish consumed per unit biomass of leatherback; or put differently how many times the population will consume its own mass in jellyfish:  eq. 14)  t  N 1  Q B    Qdt t 0  t  N 1   Bdt t 0  where ‗ Q ‘ has the units year-1. B  Uncertainty and statistical analysis Equations 1, 2, and 6 were fitted using nonlinear and linear least squares regression in SigmaPlot® 10.0 (Systat Software Inc., 2006) producing best-fit parameters for ‗k‘, ‗b‘, and ‗β‘ with ± 1 SEM. Confidence bands were included depicting how well the curve fits the data (i.e., 95% confidence that the best-fit curve is within the bands) (Motulsky and Christopoulos 2004).  98  The best-fit parameters from eqs. 1, 2, and 6 were used in eq. 8 and uncertainty around the resulting relationship was determined by taking ± 2 SEM for fit parameters ‗k‘, ‗b‘, and ‗β‘ (upper and lower confidence bands). We performed a sensitivity analysis to determine if changes in the parameters caused a positive or negative effect to the outcome of the plot. We then paired either plus 2 SEM or minus 2 SEM for each parameter to obtain best or worst case scenarios. For instance, a positive increase in ‗β‘ decreased the output for food intake (F1,t) while positive increases in ‗k‘ and ‗b‘ increased food intake. Consequently, to get the upper band for ‗F1,t‘ we combined minus 2 SEM for parameter ‗β‘ and plus 2 SEM for parameters ‗k‘ and ‗b‘ (Fig. 4.2). The output for food intake (eq. 8) was most sensitive to changes in ‗β‘ and least sensitive to ‗k‘ and ‗b‘, i.e., β >> k > b. The uncertainty around eq. 12 ( Q  ) and eq. 13 ( B  ) (Figs. 4.5 A & B) was determined by multiplying the upper and lower bands around ‗F1,t‘ and ‗Wt‘ by low, average, and high mortality estimates (Nt, eq. 11), respectively. The uncertainty around eq. 10 (MR) (Fig. 4.3) was determined by using the upper and lower bounds for ‗F1,t‘ in conjunction with the lower and upper bands for ‗K1‘.  RESULTS & DISCUSSION  Growth and food consumption Equations 1 and 2 combined gave the following VBGF (in kg) for mass,    Wt  317  1  e 0.299t 0.15    2.86  , where ‗k‘ = 0.299 ± 0.001 (t=265.17, p<0.0001) and ‗b‘ = 2.86 ±  0.014 (t=206.03, p<0.0001). ‗W∞‘ (317 kg) was obtained from eq. 2 using the asymptotic length (143 cm SCL) from Jones et al. (in review) (Chapter 3).  99  Food conversion efficiency (K1) in the form of eq.6 is depicted in Figure 4.1, where the slope of the line, ‗β‘ = 0.0328 ± 0.001 (t=35.26, p<0.0001). Plotting eq. 8 with growth parameters ‗k‘ and ‗b‘ and food conversion parameter ‗β‘ gives food intake rate as a function of age (Fig. 4.2). Integration of the area under the curve in Figure 4.2 indicates that from hatching, a leatherback will require 290 tonnes of jellyfish to attain mean nesting length.  Metabolic rate and validation of the model We assumed an assimilation efficiency of 80% for jellyfish (Wallace et al. 2006, Hatase and Tsukamoto 2008) and squid (Adam 1984, Adam et al. 1993, Rosen and Trites 2000) and determined MR using eq. 10. We plotted it against resting and field metabolic rates from the literature (Figure 4.3). As can be seen in Figure 4.3 the MR determined from food consumption coincides with MR determinations from the literature giving independent validation of estimates of food intake rates, growth, and food conversion rates.  Further validation comes from looking at total energy stored in a leatherback. Using total body water values of 73.9% for adult leatherbacks (Wallace et al. 2005) and 21.1 kJ g-1 DM of homogenized body tissue (Jones et al. 2007) an adult leatherback (317 kg) is ‗made up‘ of 1,746 MJ of energy. Given the average gross food conversion rate of a leatherback from hatching to maturity (β from eq. 6, Figure 4.1) and the average energy of gelatinous food, this suggests that a leatherback needs to consume ~ 266 tonnes of jellyfish from hatching until reaching size at maturity (15.3 years, Jones et al. in review, Chapter 3). This estimate falls within the range of our previous calculations (290 tonnes of jellyfish, eq. 8, Figure 4.2) further lending support to our model of resource requirements in leatherbacks.  100  Mortality, food consumption and biomass for the Pacific population Combining data on nesting ecology suggests that 294,153 hatchlings (Table 4.1) enter the Pacific Ocean from nesting beaches (Figure 4.4) each year, 73,538 survive their first year (juveniles), and 1,347 sub-adults recruit to the adult reproductive population each year. Inserting these data 1.38t into eq. 11 we obtain a mortality estimate of N t  294,153  e for hatchlings to yearlings,  N t  73,538  e 0.28t 1 for yearlings to sub-adults, and N t  1,347  e 0.3t 15.3 for adults. Taking into consideration high and low estimates of hatchling production, we obtained the following variance in mortality for hatchlings to yearlings and yearlings to sub-adults (Table 4.1) 0  t  1{N t  471,541  e 1.38t and N t  162,122  e 1.38t , and 1  t  15.3{N t  117,885  e 0.31t 1  and N t  40,531  e 0.24t 1 , respectively. Integration of the area under the curve suggests that there are 417,374 immature leatherbacks and 4,489 adults in any given year throughout the Pacific Ocean. For hatchlings, juveniles and adults, half-lives are 0.5, 2.48, and 2.31 years and annual mortality rates (A) are 0.75, 0.24 and 0.26.  The food consumption rate of the population of Pacific leatherbacks ( Q  , eq. 12) and their total biomass ( B  , eq. 13) are shown in Figure 4.5 - as the output of multiplying mortality (eq. 11) with food consumption rate (eq. 8; Fig 4.5B) and with the VBGF for mass (eq. 1; Fig 4.5A), respectively. Results show that the Pacific population consumes 3.4 x 106 tonnes of jellyfish per year (equivalent to 6.8 x 108 megajoules (MJ)); the greatest proportion of which, 47%, is being eaten by 2-6 year old juveniles (1.6 x 106 tonnes of jellyfish or 3.2 x 108 MJ per year), whilst, adults only account for < 4% of the total population consumption (1.3 x 105 tonnes of jellyfish or 2.6 x 107 MJ). Similarly, at 15,934 tonnes, 2-6 year old juveniles account for most of the Pacific  101  leatherback‘s population biomass (estimated at 35,393 tonnes). Immature turtles total 33,838 tonnes while adults make up less than 5% of the total population biomass (1,554 tonnes).  Jellyfish consumption per unit biomass ‗ Q ‘ (eq. 14) for the Pacific population is estimated to B be 100 year-1, thus the population would consume 100 times its biomass in jellyfish each year. Averaged over the entire population age structure this is equivalent to leatherbacks eating 27% of their body mass in jellyfish per day; the % consumption per day would be higher in growing juveniles and lower in reproductive adults which have growth rates near zero. For the latter, our data suggest that, on average, adult leatherback turtles (250-450 kg) consume 82 kg of jellyfish per day to meet daily energetic demands. This estimate agrees with a recent study in which the authors calculated that eastern Pacific and North Atlantic leatherbacks require 70 to 90 kg of jellyfish per day (Wallace et al. 2006). Earlier reports had indicated that leatherbacks must consume their body mass of jellyfish each day (Lutcavage and Lutz 1986). However, these estimates were scaled from the energetic demands of hatchlings in which the costs of growth are high. Davenport (1998) suggested that leatherbacks consume 50% of their body mass per day in jellyfish, based on direct observations of foraging leatherbacks (Duron-Dufrenne 1987) and taking into account the energetic cost of warming cold gelatinous prey items.  How does this level of predation by leatherbacks compare to natural abundance levels of jellyfish? Declines in the world‘s fish stocks and the fishing down of marine food webs (Pauly et al. 1998) has been linked to the proliferation of jellyfish (Pauly et al. 2000). With the removal of their top pelagic predators, studies have postulated that jellyfish out compete fish for resources (Lynam et al. 2005). As jellyfish abundance increases the trend worsens as jellyfish prey upon 102  fish eggs and larvae, reducing recruitment of fish to the stock (Lyman et al. 2006). Warming climatic patterns and eutrophication may further be fueling the rapid and vast expansion of jellyfish numbers (Mills 2001). Reported global increases in jellyfish, however, may not represent an increase in leatherback prey availability. In a recent study Lyman et al. (2006) reported large densities of Cnidarians Chrysaora hysoscella and Aequorea forskalea. C. hysoscella (Scyphomedusae) is a known forage item of leatherbacks (Duguy 1982) but A. forskalea, a Hydromedusae which made up 99% of the densities reported in Lyman et al. (2006) is not. To date we only know of one report indicating that leatherbacks forage on Hydromedusae (Siphonophora) (Den Hartog and Van Nierop 1984) and one report where consumption of ctenophores were observed (Salmon et al. 2004). The majority of reported leatherback prey items consist of the Phylum Cnidaria, Class Scyphomedusae (i.e., true jellyfish) including Aurelia spp., Chrysaora spp., Cyanea spp., Rhizostoma spp., and Stomolophus spp. (Duguy 1982, Duron-Dufrenne 1987, Grant and Ferrel 1993, Davenport 1998, James and Herman 2001, Salmon et al. 2004). While it seems that the gelatinous diet of leatherbacks is varied across several Phyla and Classes it is unknown if the current increases in jellyfish will aid in their recovery as many of the jellyfish blooms are invasive species (Mills 2001), not known to currently be eaten by leatherbacks, and mostly coastal in nature (Purcell 2005, Witt et al. 2007).  Lyman et al. (2006) have shown that coastal densities off Atlantic Africa are up to 361 tonnes of jellyfish n·mi-2 (per square nautical mile). Assuming these densities to also be representative of the Pacific Ocean, the entire leatherback population‘s yearly consumption could be obtained from 9,418 n·mi-2 (the equivalent of about the size of Vancouver Island in surface area). Even if the higher leatherback jellyfish intake rates (200 kg day-1) of Davenport (1998) are used then a  103  square nautical mile (Lyman et al. 2006) could feed a leatherback for 1,805 days. Oceanic waters are far less productive than coastal zones (Saba et al. 2008a,b), and jellyfish aggregations are likely to be more widely distributed - but see Purcell et al. (2000) who noted jellyfish aggregations in the North Pacific that numbered in the hundreds to millions of Aurelia spp., a known prey of leatherbacks (Duguy 1982, Salmon et al. 2004). How stable these jellyfish aggregations are, however, is unknown and it seems more likely that, in the ocean, seasonal (temporal) and spatial fluctuations in jellyfish densities (Mills 2001) will occur. As such, for the same amount of energy consumed, whilst in oceanic waters, leatherbacks probably expand more energy migrating between food patches than when inhabiting the coastal zones. Unfortunately, leatherbacks entering coastal foraging grounds will be subject to high mortality rates due to artisanal fisheries (Kaplan 2005, Alfaro-Shigueto et al. 2007) so any biological advantage will likely be lost (Saba et al. 2008b).  Consequently, it is possible that leatherbacks have a much larger role to play in the ecosystem; were leatherbacks to be restored to abundance levels known to be common two decades ago ( ~ 180,000) we estimate that the Pacific population would consume upwards of 136 x 106 tonnes of jellyfish per year. This intake would require foraging over 370,810 square nautical miles (an area slightly larger than South Africa), at jellyfish densities reported by Lyman et al. (2006). Hence, increase in leatherback numbers could reduce jellyfish abundance through consumption and save the very fisheries that threaten to drive leatherbacks to extinction.  104  Table 4.1 Total number of hatchlings entering the Pacific Ocean each year calculated from nesting ecology data from the literature. Low and high values are ± 2 SEM, or from a range when SEM not given. low  mean  high  study  nesting females per year eastern Pacific (EP)  248  248  248  nests per year (x) number eggs per nest (x) hatching success (x)  4.3 61.3 0.39  6.1 64.1 0.47  7.9 66.9 0.55  Spotila et al. 1996, Sarti et al . 2007, Tomillo et al . 2007 Reina et al . 2002 Reina et al . 2002 Spotila et al . 1996, Bell et al . 2004, Tapilatu and Tiwari 2007  25,494  45,576  72,089  1113 4.3 73.2 0.39  1113 6.1 77.9 0.47  1113 7.9 82.6 0.55  248,577  399,452  total hatchlings (EP): nesting females per year western Pacific (WP) nests per year (x) number eggs per nest (x) hatching success (x)  total hatchlings (WP): 136,628 total hatchling production for the Pacific:  162,122  294,153  471,541  first year survivorship = number of yearlings  0.25 40,531  0.25 73,538  0.25 117,885  Dutton et al . 2007 Reina et al . 2002 Tapilatu and Tiwari 2007 Spotila et al . 2004, Bell et al . 2004, Tapilatu and Tiwari 2007  Spotila et al . 1996  105  0.20  -log*(1-K1)  0.15  0.10  0.05  0.00 2.0  2.5  3.0  3.5  4.0  4.5  log mass (g) Figure 4.1 Log-log transformation of food conversion efficiency ‗K1‘ showing the best-fit curve with 95% confidence bands.  106  Jellyfish intake (tonnes yr-1)  3.5e+6 35 3.0e+6 30 2.5e+6 25 < 1 turtle  age-at-maturity  2.0e+6 20 1.5e+6 15 1.0e+6 10  5.0e+5 5 0.0  0 0  10  20  30  40  Age (years) Figure 4.2 Rate of food consumption as a function of age (F1,t), giving the food intake of a single leatherback at any age throughout its life-history. Arrows show age-at-maturity (15.3 years) (Jones et al. in review) and age when there is < 1 leatherback (39.5 years) from mortality estimates. Upper and lower bands for ‗F1,t‘ were determined by combining minus and plus 2 SEM for parameters ‗β‘, ‗k‘ and ‗b‘ (see methods section).  107  Mass-specific MR (W kg-1)  2.0 This study, determined from food intake Jones et al. 2007 (MMR) Jones et al. 2007 (RMR) Hastings 2006 (RMR) Paladino et al. 1990 (RMR) Lutcavage et al. 1992 (RMR) Paladino et al. 1996 (RMR) Wallace et al. 2005 (FMR)  1.5  1.0  0.5  0.0 0  100 1e+5  200 2e+5  300 3e+5  400 4e+5  Mass (kg) Figure 4.3 Metabolic rate (MR) (W kg-1) determined from food consumption plotted with metabolic rates of leatherback hatchlings, juveniles, and adults from the literature. Upper and lower bands were determined by using the upper and lower bands for Figures 4.1 and 4.2. (see methods section).  108  Figure 4.4 Red arrows show location of the eastern and western Pacific leatherback nesting rookeries. Adapted from SWoT Report Volume 1, Conservation International, www.seaturtlestatus.org  109  Biomass (tonnes)  8e+9 8,000  6e+9 6,000  4e+9 4,000  2e+9 2,000  0  0  -1  Jellyfish intake (tonnes yr )  A  8e+10 800,000 B  6e+10 600,000  4e+10 400,000  2e+10 200,000  0  0 0  10  20  Age (years) Figure 4.5 Total Pacific leather population biomass (A); and consumption rates in tonnes of jellyfish per year for the entire Pacific leatherback population (B). The uncertainty around A & B was determined by multiplying the upper and lower bands around ‗F1,t‘ and ‗Wt‘ by low, average, and high mortality estimates (Nt, eq. 11), respectively (see methods section). 110  REFERENCES Adams NJ (1984) Utilisation efficiency of a squid diet by adult king penguins (Aptenodytes patagonicus). Auk 101(4): 884-886 Adams NJ, Moloney C, Navarro R (1993) Estimated food consumption by penguins at the Prince Edward Islands. Antarctic Science 5(3): 245-252 Alfaro-Shigueto J, Dutton PH, Van Bressman M-F, Mangel J (2007) Interactions between leatherback turtles and Peruvian artisanal fisheries. Chelonian Conservation and Biology 6(1): 129-134 Bell BA, Spotila JR, Paladino FV, Reina RD (2004) Low reproductive success of leatherback turtles, Dermochelys coriacea, is due to high embryonic mortality. Biological Conservation 115: 131-138 Benson SR, Dutton PH, Hitipeuw C, Samber B, Bakarbessy J, Parker D (2007) Post-nesting migrations of leatherback turtles (Dermochelys coriacea) from Jamursba-Medi, Bird‘s Head Peninsula, Indonesia. Chelonian Conservation and Biology 6: 150-154 Bjorndal KA (1997) Foraging ecology and nutrition of sea turtles. In: Lutz P, Musick J (eds) The Biology of Sea Turtles. CRC Press, Boca Raton. pp. 199-232 Bleakney JS (1965) Reports of marine turtles from New England and eastern Canada. Canadian Field Naturalist 79: 120-128 Bostrom BL, Jones DR (2007) Exercise warms adult leatherback turtles. Comparative Physiology Biochemistry Part A 147: 323–331 Brongersma LD (1969) Miscellaneous notes on turtles, IIA, IIB, Proceedings. Koninlijke Nederlandse Akademie van Wetenschappen (ser C) 72: 90-102  111  Congdon JD, Dunham AE, Tinkle DW (1982) Energy budgets and life histories of reptiles. In: Gans C, Pough FH (eds) Biology of the Reptilia, Vol 13. Academic Press, New York, pp 233–271 Davenport J, Balazs GH (1991) ―Fiery pyrosomas‖ - are pyrosomas an important item in the diet of leatherback turtles? British Herpetological Societal Bulletin 37: 33-38 Davenport J (1998) Sustaining endothermy on a diet of cold jelly: energetics of the leatherback turtle Dermochelys coriacea. British Herpetological Society Bulletin 62: 4-8 Den Hartog JC, Van Nierop MM (1984) A study on the gut contents of 6 leathery turtles Dermochelys coriacea (Linnaeus) (Reptilia: Testudines: Dermochelyidae) from British Waters and The Netherlands. Zoologische Verhandlingen 209: 1–31 Doyle TK, Houghton JDR, McDevitt R, Davenport J, Hays GC (2007) The energy of jellyfish: estimates from bomb-calorimetry and proximate-composition. Journal of Experimental Marine Biology and Ecology 343: 239-252 Duguy R (1982) Note sur les méduses des Pertuis Charentais. Annales de la Sociéte des Sciences Naturelles de la Charente-Maritime 6: 1029–1034 Duron-Dufrenne M (1987) Premier suivi par satellite en Atlantique d‘une tortue luth Dermochelys coriacea. Comptes Rendus de l‘Academie des Sciences Paris 304:339–402 Dutton PH, Hitipeuw C, Zein M, Benson SR, Petro G, Pita J, Rei V, Ambio L, Bakarbessy J (2007) Status and genetic structure of nesting populations of leatherback turtles (Dermochelys coriacea) in the western Pacific. Chelonian Conservation and Biology 6(1): 47-53 Eckert KL, Luginbuhl C (1988) Death of a giant. Marine Turtle Newsletter 43: 1-3  112  Eckert SA, Bagley D, Kubis S, Ehrhart L, Johnson C, Stewart K, DeFresse D (2006) Internesting and postnesting movements and foraging habitats of leatherback sea turtles (Dermochelys coriacea) nesting in Florida. Chelonian Conservation and Biology 5: 239–248 Goff GP, Lien J (1988) Atlantic leatherback turtles, Dermochelys coriacea, in cold water off Newfoundland and Labrador. Canadian Field Naturalist 102: 1-5 Grant GS, Ferrell D (1993) Leatherback turtle, Dermochelys coriacea (Reptilia, Dermochelidae)—notes on near-shore feeding behavior and association with Cobia. Brimleyana 19: 77–81 Hartog JC den, Nierop MM van (1984) A study on the gut contents of six leathery turtles Dermochelys coriacea (Linnaeus) (Reptilia: Testidunes: Dermochelydae) from British waters and from the Netherlands. Zoologische Verhandlingen 200: 36 Hatase H, Tsukamoto K (2008) Smaller longer, larger shorter: energy budget calculations explain intrapopulation variation in remigration intervals for loggerhead sea turtles (Caretta caretta). Canadian Journal of Zoology 86: 595-600 Hawkes LA, Broderick AC, Coyne MS, Godfrey MH, Lopez-Jurado L, Lopez-Suarez P, Merino S, Varo-Cruz N, Godley B (2006) Phenotypically linked dichotomy in sea turtle foraging requires multiple conservation approaches. Current Biology 16: 990–995 Hays GC, Houghton JD, Myers AE (2004) Pan-Atlantic leatherback turtle movements. Nature 429: 522 Hays GC, Hobson VJ, Metcalfe JD, Righton D, Sims DW (2006) Flexible foraging movements of leatherback turtles across the North Atlantic Ocean. Ecology 87: 2647–2656  113  Houghton JDR, Doyle TK, Wilson MW, Davenport J, Hays GC (2006) Jellyfish aggregations and leatherback turtle foraging patterns in a temperate coastal environment. Ecology 87(8): 1967-1972 IUCN 2008. 2008 IUCN Red List of Threatened Species. <www.iucnredlist.org>. Downloaded on 20 February 2009. Ivlev VS (1966) The biological productivity of waters. Ricker WE (translation). Journal of Fishery Research Board of Canada 23(11): 1727-1759 James MC, Herman TB (2001) Feeding of Dermochelys coriacea on medusae in the Northwest Atlantic. Chelonian Conservation Biology 4: 202–205 James MC, Myers RA, Ottensmeyer CA (2005) Behaviour of leatherback sea turtles, Dermochelys coriacea, during the migratory cycle. Proceedings of the Royal Society London B Biological Sciences 272: 1547–1555 Jones DR, Southwood AL, Andrews RD (2004) Energetics of leatherback sea turtles: a step toward conservation. In: Gordon MS and Bartol SM (eds) Experimental Approaches to Conservation Biology. Berkeley, CA: University of California Press. pp. 66-82 Jones TT, Reina RD, Darveau C-A, Lutz PL (2007) Ontogeny of energetics in leatherback (Dermochelys coriacea) and olive ridley (Lepidochelys olivacea) sea turtle hatchlings. Comparative Biochemistry Physiology Part A 147: 313-322 Kaplan IC (2005) A risk assessment for Pacific leatherback turtles (Dermochelys coriacea). Canadian Journal of Fisheries and Aquatic Sciences 62: 1710-1719 Lutcavage ME, Lutz PL (1986) Metabolic rate and food energy requirements of the leatherback sea turtle, Dermochelys coriacea. Copeia 1986(3): 796–798  114  Lutcavage ME, Bushnell PG, Jones DR (1992) Oxygen stores and aerobic metabolism in the leatherback sea turtle. Canadian Journal of Zoology 70: 348-351 Lynam CP, Heath MR, Hay SJ, Brierley AS (2005) Evidence for impacts by jellyfish on North Sea herring recruitment. Marine Ecology Progress Series 298: 157–167 Lynam CP, Gibbons MJ, Axelsen BE, Sparks CAJ, Coetzee J, Heywood BG, Brierley AS (2006) Jellyfish overtake fish in heavily fished ecosystem. Current Biology 16(13): R492-493 Mills CE (2001) Jellyfish blooms: are populations increasing globally in response to changing ocean conditions? Hydrobiologia 451: 55–68 Motulsky H, Christopolous A (2004) Fitting models to biological data using linear and nonlinear regression: A practical guide to curve fitting. Oxford University Press, Inc. New York Nagy KA (1989) Field bioenergetics: accuracy of models and methods. Physiological Zoology 62(2): 237-252 Paladino FV, O‘Connor MP, Spotila JR (1990) Metabolism of leatherback turtles, gigantothermy, and thermoregulation of dinosaurs. Nature 344: 858-860 Paladino FV, Spotila JR, O‘Connor MP, Gatten RE, Jr (1996) Respiratory physiology of adult leatherback turtles (Dermochelys coriacea) while nesting on land. Chelonian Conservation Biology 2: 223-229 Pauly D (1986) A simple method for estimating the food consumption of fish populations from growth data and food conversion experiments. Fishery Bulletin 84(4): 827-842 Pauly D, Sambilay V JR, Opitz S (1993) Estimates of relative food consumption by fish and invertebrate populations, required for modeling the Bolinao reef ecosystem, Philippines. In: Christensen V, Pauly D (eds) Trophic models of aquatic ecosystems. ICLARM Conference Proceedings 26, 236-251  115  Pauly D, Christensen V, Dalsgaard J, Froese R, Torres F (1998) Fishing down marine food webs. Science 279: 860–863 Pauly D, Christensen V, Guenette S, Pitcher TJ, Sumaila UR, Walters CJ, Watson R, Zeller D (2002) Towards sustainability in world fisheries. Nature 418: 689–695 Purcell JE, Brown ED, Stokesbury KDE, Haldorson LH, Shirley TC (2000) Aggregations of the jellyfish Aurelia labiata: abundance, distribution, association with age-0 walleye Pollock, and behaviors promoting aggregation in Prince William Sound, Alaska, USA. Marine Ecology Progress Series 195: 145-158 Purcell JE (2005) Climate effects on jellyfish and ctenophore blooms: a review. Journal Marine Biological Association of U.K. 85: 461–476 Reina RD, Mayor PA, Spotila JR, Piedra R, Paladino FV (2002) Nesting ecology of the leatherback turtle, Dermochelys coriacea, at Parque Nacional Marino Las Baulas, Costa Rica: 1988-1989 to 1999-2000. Copeia 2002: 653-664. Ricker WE (1975) Computational and interpretation of biological statistics of fish populations. Bulletin 191 Department of the Environment Fisheries and Marine Service, Ottawa Rosen DAS, Trites AW (2000) Digestive efficiency and dry-matter digestibility in Steller sea lions fed herring, Pollock, squid, and salmon. Canadian Journal of Zoology 78: 234-239 Saba VS, Santidrian-Tomillo P, Reina RD, Spotila JR, Musick JA, Evans DA, Paladino FV (2007) The effect of the El Nino Southern Oscillation on the reproductive frequency of eastern Pacific leatherback turtles. Journal of Applied Ecology 44: 395-404 Saba VS, Spotila JR, Chavez FP, Musick JA (2008a) Bottom-up and climatic forcing on the worldwide population of leatherback turtles. Ecology 89: 1414-1427  116  Saba VS, Shillinger GL, Swithenbank AM, Block BA, Spotila JR, Musick JA, Paladino FV (2008b) An oceanographic context for the foraging ecology of eastern Pacific leatherback turtles: consequences of ENSO. Deep-Sea Research Part 1 55: 646-660 Salmon M, Jones TT, Horch K (2004) Ontogeny of diving and feeding behavior in juvenile sea turtles: A comparison study of green turtles (Chelonia mydas L.) and leatherbacks (Dermochelys coriacea L.) in the Florida current. Journal of Herpetology 38: 36-43 Sarti-Martinez L, Barragañ A, Garcia-Muñoz D, Garcia N, Huerta P, Vargas F (2007) Conservation and biology of the leatherback turtle in the Mexican Pacific. Chelonian Conservation and Biology 6: 70–78 Sherwood L (2005) Fundamentals of physiology: a human perspective. Thomson Brooks/ Cole pp. 736 Speakman JR (1997) Doubly Labelled Water: Theory and Practice. London: Chapman & Hall. pp. 399 Spotila JR, Dunham AE, Leslie AJ, Steyermark AC, Plotkin PT Paladino FV (1996) Worldwide population decline of Dermochelys coriacea: are leatherback turtles going extinct? Chelonian Conservation and Biology 2: 209-222 Spotila JR, Reina RD, Steyermark AC, Paladino FV (2000) Pacific leatherback turtles face extinction. Nature 405: 529-530 Steyermark AC, Williams K, Spotila JR, Paladino FV, Rostal DC, Morreale SJ, Koberg MT, Arauz R (1996) Nesting leatherback turtles at Las Baulas National Park, Costa Rica. Chelonian Conservation and Biology 2(2): 173-183  117  Tapilatu RF and Tiwari M (2007) Leatherback turtle, Dermochelys coriacea, hatching success at Jamursba-Medi and Wermon Beaches in Papua, Indonesia. Chelonian Conservation and Biology 6(1): 154–158 Tomillo PS, Velez E, Reina RD, Piedra R, Paladino FV, Spotila JR (2007) Reassessment of the leatherback turtle (Dermochelys coriacea) nesting population at Parque Nacional Marino Las Baulas, Costa Rica: effects of conservation efforts. Chelonian Conservation and Biology 6: 54-62 von Bertalanffy L (1938) A quantitative theory of organic growth (Inquiries on growth laws. II.). Human Biology 10(2): 181-213 Wallace BP, Williams CL, Paladino FV, Morreale SJ, Lindstrom RT, Spotila JR (2005) Bioenergetics and diving activity of interesting leatherback turtles Dermochelys coriacea at Parque Nacional Marino Las Baulas, Costa Rica. Journal of Experimental Biology 208: 3873–3884 Wallace BP, Kilham SS, Paladino FV, Spotila JR (2006) Energy budget calculations indicate resource limitation in eastern Pacific leatherback turtles. Marine Ecology Progress Series 318: 263-270 Wallace BP and Jones TT (2008) What makes marine turtles go: A review of metabolic rates and their consequences. Journal Experimental Marine Biology and Ecology 356: 8-24 Witt MJ, Broderick AC, Johns DJ, Martin CS, Penrose R, Hoogmoed MS, Godley BJ (2007) Prey landscapes help identify potential foraging habitats for leatherback turtles in the northeast Atlantic. Marine Ecology Progress Series 337: 231–244  118  Wyneken J (1997) Sea turtle locomotion: mechanisms, behavior, and energetics. In: Lutz PL, Musick JA (Eds.) The Biology of Sea Turtles, vol. 1. CRC Press, Boca Raton, FL, pp. 165– 198 Zug GR, Parham JF (1996) Age and growth in leatherback turtles, Dermochelys coriacea (Testudines: Dermochelyidae): a skeletochronological analysis. Chelonian Conservation and Biology 2(2): 244-249  119  CHAPTER FIVE: GENERAL DISCUSSION The goal of the research presented in this thesis was to determine the energetics of leatherback turtles and provide context for the role of physiology in conservation efforts. While Chapter 2 is experimental in nature Chapters 3 and 4 are descriptive and observational. This is due in part to the critically endangered status of leatherbacks (IUCN 2008) and the difficulty in obtaining permits to hold and conduct research on hatchlings and juveniles. Leatherback juveniles are not easy subjects to work with and a majority of my efforts were expended doing husbandry. There is no doubt, however, of the ecological importance of my data. A small group of 20 hatchlings from a nest of 60-80 eggs has given us valuable and unique information about growth rates, ageat-maturity estimates, energy requirements and MR throughout development. Furthermore, my data has allowed an estimate of total biomass and total food consumption rates at the population level. It is certainly probable that the time, energy, and money required for such an endeavor as I have conducted may preclude a project of this magnitude from happening again for a generation or more.  To address the goal of determining daily energy requirements of leatherbacks I began by looking into methods to measure their at-sea metabolic rate. The use of the DLW method for determining FMR has been gaining wide scale application in the ecological studies of animals and I explored its use in marine turtles. When I started my research there were no published studies on the use of DLW in marine turtles, but since I began there have been three publications reporting its use (Wallace et al. 2005, Trullas et al. 2006, Southwood et al. 2006). Aquatic animals, however, may be bad candidates for measurement of FMR using DLW as water turnover rates can be as high as 100 to 400% of total body water (TBW) day-1 (Booth 2002). While water turnover rates 120  in marine turtles are an order of magnitude less than this, they still may be too high to get detectable divergence between isotopic decay rates of deuterium (associated with water flux) and 18-O (associated with water and CO2 flux). Yet, the three studies on marine turtles mentioned above were published without any attempt at validating the method.  Use of DLW in marine mammal studies has been similarly controversial as DLW-derived FMRs have been estimated at 5-7 times higher than when measured by time energy budgets (Costa et al. 1989, Reilly and Fedak 1991, Arnould et al. 1996) leading to questions of the validity of the method as the FMRs from DLW studies are close to physiologically attainable maximums. A recent study by Sparling et al. (2007) validated the DLW method against open-flow respirometry in marine mammals as I have done for marine turtles. Both studies are similar in their finding that the % difference between average MRs for the two methods (i.e. DLW and respirometry) are < 8% but the error of the methods within individual animals can range ± 100%. Unfortunately, this tends to be the case in studies seeking to validate the DLW method (Butler et al. 2004). However, a novel finding in my research is that when turtles were starved, the washout rate of deuterium exceeded that of the oxygen label resulting in negative MRs. This was the most salient finding of the study as it indicates that the DLW method may not be applicable to all physiological states. The fact the method proves not to work in some situations is a rare finding that will be of broad interest to all people applying the method. Furthermore, these results highlight that previous findings need to be evaluated with caution and future studies on marine turtles will need to consider feeding regimen in their assessment of FMR.  121  There is evidence supporting feeding (Southwood et al. 2005; Fossette et al. 2008) and fasting (Hays et al. 2002; Reina et al. 2005) of marine turtles during the breeding season. This may constrain the use of DLW for internesting turtles as periods of feeding and fasting would cause changes in %TBW, water flux rates and possibly FMR (Table 2.1) all of which can increase error in DLW-derived FMRs. It should be noted however that (i) even in the fed state two of my measurements could not be used due to high kd:ko ratios and (ii) for 4 turtles the average ratio was 0.91±0.02 (Table 2.1). Therefore, even in green turtles that have the lowest measured water turnover rate of all marine turtles (Ortiz et al. 2000 , Wallace et al. 2005, Southwood et al. 2006, this study) the use of DLW may be constrained by their low MR and high water turnover. Wallace et al. (2005) were able to measure DLW-derived FMR in 3 out of 5 leatherbacks, even though leatherbacks have the highest measured water turnover rates of the marine turtles at 24% TBW day-1 but of the measurements several of them had kd:ko ratios above 0.9 with one turtle washing out all isotopes and the other giving a negative FMR. The average FMR for the 3 leatherbacks was 34.56 ± 17.28 kJ kg-1 day-1, a value similar to what I measured for greens (30.85 ± 21.02 kJ kg-1 day-1; Table 2.1). The fact that recorded leatherback FMRs are not elevated within the marine turtle group (Wallace and Jones 2008, Chapter 2), as was thought previously, indicates a further constraint on use of the DLW method for leatherbacks as their water turnover rates are higher than any other marine turtle.  Finally, the extreme cost of a single DLW application in adult leatherbacks, combined with the real possibility of failure, negates its use as a realistic approach in field studies. I therefore switched focus and began to look at alternative methods to determine leatherback energetics. I pursued raising leatherbacks in the laboratory and determined protocols that allowed me to raise  122  multiple leatherbacks, a task never before accomplished for longer than 100 days. The oldest turtle attained 2.5 years of age, 43 kg and 72 cm SCL.  The reared leatherbacks matched a length-mass relationship for wild juveniles and adults (Figure 3.1) allowing inferences to growth in the wild. Applying three different growth functions (VBGF, Gompertz, and logistic) to the growth data suggested age-at-maturity estimates ranging from 4 to 15 years (Table 3.5). Growth rates of the captive leatherbacks were 31.9 ± 2.8 cm SCL year-1 (Table 3.4), suggesting early attainment (4 years), as did the logistic growth function, of mean nesting length for leatherbacks (147 cm SCL) (Stewart et al. 2007). Analysis of growth data using the VBGF, however, gave the best fit suggesting that growth rate declined with age and gave an age-at-maturity estimate of 15.3 years. While this is longer than previous reports of 3-6 years (Birkemeier 1971, Rhodin 1985) it still places leatherbacks as the fastest growing marine turtle. Kemp‘s ridley turtles have age-at-maturity estimates of 11-16 years (Zug et al. 1997) but their mean nesting size is 65 cm SCL (80 cm less than leatherbacks) and at 14.5 cm SCL year-1 their first year growth rates are less than half of those registered in leatherbacks. The most salient finding, however, was that all three growth functions and measured growth rates (Table 3.4) indicated that leatherbacks will reach > 70 cm SCL within 3 year of age, the length at which leatherbacks begin to fatally interact with fisheries. If conservation efforts are to ensure that leatherbacks are brought back from the brink of extinction, radical measures will have to be put in place to remove, or at the very least drastically minimize fisheries‘ impacts on leatherbacks for the majority of their lives.  123  Having obtained growth rates and age-at-maturity estimates I was then able to couple growth with feeding experiments and food consumption functions to determine daily energetic requirements of leatherbacks throughout development. Construction of mortality rates from data in the literature allowed me to expand food consumption (energy requirements) of the individual to consumption rates at the level of the entire Pacific population. This was then placed in the context of available resources (jellyfish abundance).  Consumption rates obtained for a single individual suggest that a leatherback will eat upward of 1,000 tonnes of jellyfish in its lifetime. This is perhaps the most holistic way to look at leatherback energetics as the energy requirements were estimated by integrating consumption from hatching to maturity. To my knowledge this quantitative approach to measuring energy requirements of leatherbacks has not been used in any other study on marine turtles. I validated the technique by deriving MR from food consumption and subsequently comparing obtained values with ones from the literature (Figure 4.4). The excellent agreement between my estimates and directly recorded values clearly demonstrated the power and value of using the computational method to help elucidate marine turtle energetics.  Bradshaw et al. (2007) determined FMR by analyzing dive profiles of leatherback turtles in conjunction with known oxygen stores (Lutcavage et al. 1992). They concluded that the leatherbacks dive within but close to their aerobic dive limit, therefore dividing total oxygen stores by mean length of the extended dives will give MR. Bradshaw et al. (2007) determined FMR to be 21 kJ kg-1 day-1, a value within the range of MR measured from nesting leatherbacks by respirometry (Paladino et al. 1990, Lutcavage et al. 1992, Paladino et al. 1996), DLW-  124  derived FMR (Wallace et al. 2005) and through computational analysis of food conversion (31 kJ kg-1 day-1; this study). The study by Bradshaw et al. (2007) and this study highlight the relevance & applicability of computational modeling to deriving estimates of FMR.  The most salient finding is 2-6 year old juveniles account for the largest portion of the Pacific leatherback population‘s biomass (45%; 137,368 turtles) and food consumption (1.6 x 106 tonnes of jellyfish per year; 47%). According to the VBGF (Figure 3.2) and derived length-mass relationship (Figure 3.1) a 6 year old juvenile would be 107 cm SCL, 137 kg with a MR of 0.6 W kg-1 (Figure 4.4). Plugging this mass and MR into the thermoregulatory model of Bostrom and Jones (2007) indicates that turtles of this size would be capable of maintaining a thermal gradient between body and ambient water temperature of 2-4 °C. Animals of this size would therefore be confined to the warmer waters of the sub-tropical and southern temperate oceans. These warmer waters are not as nutrient rich/productive as more temperate waters (Saba et al. 2008) (Figure 5.1). In these waters leatherbacks needing to consume 20 tonnes of jellyfish a year (55 kg day-1) would be restricted to the coastal areas or the equatorial convergence zone (Saba et al. 2008; Figure 5.1). Unfortunately, coastal areas are associated with the highest registered mortality rates for marine turtles (Kaplan 2005, Alfaro-Shigueto et al. 2007). While oceanic mortality rates are lower, commercial fisheries tend to focus their efforts in the equatorial convergence area (Lewison et al. 2004), the same area leatherbacks probably congregate to find gelatinous prey (Polovina et al. 2001; Saba et al. 2008) therefore leatherbacks are faced with threats from fisheries throughout their foraging habitats.  125  I assumed assimilation efficiency (AE) of jellyfish to be 80%, as did Wallace et al. (2006) and Hatase and Tsukamoto (2008) who based their AE on a study of slider turtles (Avery et al. 1993) fed a diet similar in protein to jellyfish (Malej et al. 1993). Jellyfish and gelatinous zooplankton (Scyphomedusae, hydromedusae, ctenophores, and tunicates) are also rich in mucopolysaccharides, long chains of sugars that can be hard to digest (Davenport and Balazs 1991). Therefore, direct studies of assimilation in leatherbacks for their various gelatinous prey types (Cnidarians, Ctenophores, and tunicates) are needed. Furthermore, as jellyfish species proportions, along with their environmental landscapes, are changing (Mills 2001, Lyman et al. 2006) it will be important to determine if leatherbacks actually select scyphomedusae over hydromedusae or other gelatinous prey. Simple behavioral experiments such as those used by Constantino and Salmon (2003) to determine the role of visual and chemical cues in hatchlings (i.e. circle tanks with tethered turtles attached to directional indicators) could be used to determine plasticity in leatherback prey choice as well as whether leatherbacks feed selectively on higher energy portions of jellyfish such as the oral arm or gonads (Doyle et al. 2007). Doyle et al. (2007) determined the energy densities for 3 species of scyphomedusae but this type of study needs to be extended to include hydromedusae, ctenophores and tunicates. Synthesizing the data on assimilation efficiency, prey selectivity and energy densities of prey will give a more complete picture of how changing jellyfish landscapes (Mills 2001, Witt et al. 2007) affect leatherback ecology.  My study allowed direct length-at-age measurements up to 70 cm SCL but length-at-age estimates from animals obtained from the field are clouded in controversy. Traditional skeletochronology (Zug and Parham 1996) or growth function studies (Chapter 3) require dead  126  animals or laboratory rearing to obtain known length-at-age data. Recently, a promising new technique for aging free living animals has been developed from measuring telomere lengths in animals of different ages (Haussman and Vleck 2002). This method has been applied to loggerhead turtles (Hatase et al. 2008) and showed a clear trend for shortening of telomeres from epidermal cells as the animals aged. These techniques should allow direct aging of free-living marine turtles although calibration will prove to be difficult in the absence of animals of known age. Genetically marking marine turtles, specifically hatchlings (Tringali 2005), would allow age-at-maturity estimates from beaches where observation of the whole nesting beach takes place (e.g. St. Croix, USVI, Dutton et al. 2005; Playa Grande, Costa Rica, Tomillo et al. 2007). If all new (first-time) nesters were genetically sampled then age-at-maturity estimates could be determined in addition to developmentally specific (pre maturity) mortality rates.  Genetically marking hatchlings could be coupled with a moratorium to determine if relieving the pressure from fishery bycatch leads to a recovery of the species. Monitoring nesting beaches after short or long-term moratoriums for increases in new nesters will indicate whether there has been a rebound in leatherback numbers. But, population recovery may not be possible. For instance, collapse of the Atlantic cod fishery (Myers et al. 1997) led to a moratorium on cod fishing in the western North Atlantic. A decade and a half later the fishery has not recovered (Brooks 2008). Unfortunately, a moratorium only puts a stop to legal fisheries. Agnew et al. (2009) have shown that illegal fishing accounts for 11 to 26 million tonnes yr-1; in some areas the illegal catch is 1.4 times the legal fishing take. These illegal fisheries significantly damage ecosystems as they commonly use longline and gillnet sets and they do not respect national and  127  international laws to reduce shark, marine turtle, bird and marine mammal bycatch (Agnew et al. 2009).  Why save leatherbacks other than to prevent further loss of biodiversity and the charge of immorality ensuing from letting an ancient mariner, such as the leatherback, go extinct. Large pelagics such as the leatherback and the sunfish play a crucial role in reducing jellyfish numbers (Hays et al. 2009). Warming climate patterns (Mills 2001) and overfishing (Pauly et al. 1998) have led to jellyfish proliferations to the point where jellyfish have replaced fish as the dominant species in some ecosystems (Dashalov 2002, Lyman et al. 2006). Therefore restoration of leatherbacks to pre-1980 abundance could reduce the numbers of gelatinous zooplankton which out compete fish for resources as well as prey directly on fish eggs and larvae (Lynam et al. 2005, 2006).  What, if any, has been the contribution of my research to saving leatherbacks? Modeling data suggests that the simplest and most direct approach to saving Pacific leatherbacks would be a moratorium on fishing in the equatorial convergence and coastal areas. It is clear from chapter 3 that there is considerable fishing mortality imposed on leatherbacks early in their development (from 3 years of age on) while chapters 3 and 4 combined indicate that the numbers poised to become sexually mature adults are in the hundreds of thousands (> 400,000). A moratorium cannot be put in place, however, without some form of compensation for the fishery which leads in today‘s economic climate to an uneven balance between what is right and what is necessary. If even the most charismatic of endangered marine species cannot be saved what hope is there for the future of our oceans? 128  CLOSING BY ISHMAEL  Finally: It was stated at the outset, that this system would not be here, and at once, perfected. You cannot but plainly see that I have kept my word. But I now leave my ‗dermochelogical‘ system standing thus unfinished, even as the great Cathedral of Cologne was left, with crane still standing upon the top of the uncompleted tower. For small erections may be finished by their first architects; grand ones, true ones, ever leave the copestone to posterity. God keep me from ever completing anything. This whole book is but a draught – nay, but the draught of a draught. Oh, Time, Strength, Cash, and Patience! ~ Ishmael (Moby Dick)  129  Figure 5.1 Sea-viewing Wide Field-of-View Sensor (SeaWiFS). Green areas indicate areas of high primary productivity (chlorophyll-a). http://oceancolor.gsfc.nasa.gov/SeaWiFS  130  REFERENCES Agnew DJ, Pearce J, Pramod G, Peatman T, Watson R, Beddington JR, Pitcher T (2009) Estimating the worldwide extent of illegal fishing. PLoS ONE 4(2): e4570. doi:10.1371/journal.pone.0004570 Avery HW, Spotila JR, Congdon JD, Fischer RU Jr, Standora EA, Avery SB (1993) Roles of diet protein and temperature in the growth and nutritional energetics of juvenile slider turtles, Trachemys scripta. Physiological Zoology 66: 902–925 Alfaro-Shigueto J, Dutton PH, Van Bressman M-F, Mangel J (2007) Interactions between leatherback turtles and Peruvian artisanal fisheries. Chelonian Conservation and Biology 6(1): 129-134 Arnould JPY, Boyd IL, Speakman JR (1996) The relationship between foraging behaviour and energy expenditure in Antarctic Fur seals. Journal of Zoology (London) 239: 769–782 Birkenmeier E (1971) Juvenile leatherback turtles, Dermochelys coriacea (Linnaeus), in captivity. Brunei Museum Journal 3(1): 160-172 Booth DT (2002) The doubly-labeled water technique is impractical for measurement of field metabolic rate in freshwater turtles. Herpetological. Revue 33: 105-107 Bostrom BL, Jones DR (2007) Exercise warms adult leatherback turtles. Comparative Physiology Biochemistry Part A 147: 323–331 Bradshaw CJA, McMahon CR, Hays GC (2007) Behavioural inference of diving metabolic rate in free-ranging leatherback turtles. Physiological and Biochemical Zoology 80(2): 209-219 Brooks C (2008) No recovery for Atlantic cod population. ScienceNOW Daily News, November 25th 2008  131  Constantino MA, Salmon M (2003) Role of chemical and visual cues in food recognition by leatherback posthatchlings (Dermochelys coriacea L) Zoology 106(3): 173-181 Costa D, Croxal J, Duck C (1989) Foraging energetics of Antarctic fur seals in relation to changes in prey availability. Ecology 70: 596–606 Daskalov GM (2002) Overfishing drives a trophic cascade in the Black Sea. Marine Ecology Progress Series 225: 53-63 Davenport J, Balazs GH (1991) ―Fiery pyrosomas‖ - are pyrosomas an important item in the diet of leatherback turtles? British Herpetological Societal Bulletin 37: 33-38 Doyle TK, Houghton JDR, McDevitt R, Davenport J, Hays GC (2007) The energy of jellyfish: estimates from bomb-calorimetry and proximate-composition. Journal of Experimental Marine Biology and Ecology 343: 239-252 Dutton DL, Dutton PH, Chaloupka M, Boulon RH (2005) Increase of a Caribbean leatherback turtle Dermochelys coriacea nesting population linked to long-term nest protection. Biological Conservation 126: 186-194 Fossette S, Corbel H, Gaspar P, LeMaho Y, Georges JY (2008) An alternative technique for the long-term satellite tracking of leatherback turtles. Endangered Species Research 4:33-41 Hatase H, Tsukamoto K (2008) Smaller longer, larger shorter: energy budget calculations explain intrapopulation variation in remigration intervals for loggerhead sea turtles (Caretta caretta). Canadian Journal of Zoology 86: 595-600 Hatase H, Sudo R, Watanabe KK, Kasugai T, Saito T, Okamoto H, Uchida I, Tsukamoto K (2008) Shorter telomere length with age in the loggerhead turtle: a new hope for live sea turtle age estimation. Genes & Genetic Systems 83(5): 423-426  132  Haussman MF, Vleck CM (2002) Telomere length provides a new technique for aging animals Oecologia 130: 325-328 Hays GC, Broderick AC, Glen F, Godley BJ (2002) Change in body mass associated with longterm fasting in a marine reptile: the case of green turtles (Chelonia mydas) at Ascension Island. Canadian Journal of Zoology 80: 1299–1302 Hays GC, Farquhar MR, Luschi P, Teo SLH, Thys TM (2009) Vertical niche overlap by two ocean giants with similar diets: Ocean sunfish and leatherback turtles. Journal of Experimental Marine Biology and Ecology doi:10.1016/j.jembe.2008.12.009 IUCN 2008. 2008 IUCN Red List of Threatened Species. <www.iucnredlist.org>. Downloaded on 20 February 2009. Kaplan IC (2005) A risk assessment for Pacific leatherback turtles (Dermochelys coriacea). Canadian Journal of Fisheries and Aquatic Sciences 62: 1710-1719 Lewison RL, Freeman SA, Crowder LB (2004) Quantifying the effects of fisheries on threatened species: the impact of pelagic longlines on loggerhead and leatherback sea turtles Lutcavage ME, Bushnell PG, Jones DR (1992) Oxygen stores and aerobic metabolism in the leatherback sea turtle. Canadian Journal of Zoology 70: 348-351 Lynam CP, Heath MR, Hay SJ, Brierley AS (2005) Evidence for impacts by jellyfish on North Sea herring recruitment. Marine Ecology Progress Series 298: 157–167 Lynam CP, Gibbons MJ, Axelsen BE, Sparks CAJ, Coetzee J, Heywood BG, Brierley AS (2006) Jellyfish overtake fish in heavily fished ecosystem. Current Biology 16(13): R492-493 Mills CE (2001) Jellyfish blooms: are populations increasing globally in response to changing ocean conditions? Hydrobiologia 451: 55–68  133  Malej A, Faganeli J, Pezdič J (1993) Stable isotope and biochemical fractionation in the marine pelagic food chain: the jellyfish Pelagia noctiluca and net zooplankton. Marine Biology 116: 565–570 Myers RA, Hutchings JA, Barrowman NJ (1997) Why do fish stocks collapse? The example of cod in Atlantic Canada. Ecological Applications 71: 91-106 Ortiz RM, Patterson RM, Wade CE, Byers FM (2000) Effects of Acute Fresh Water Exposure on Water Flux Rates and Osmotic Responses in Kemp‘s Ridley Sea Turtles (Lepidochelys kempi). Comparative Biochemicstry and Physiology Part A 127: 81 – 87 Paladino FV, O'Connor MP, Spotila JR (1990) Metabolism of Leatherback Turtles, Gigantothermy,and Thermoregulation of Dinosaurs. Nature 344: 858 Paladino FV, Spotila JR, O‘Connor MP, Gatten Jr. RE (1996) Respiratory Physiology of Adult leatherback Turtles (Dermochelys coriacea) While Nesting on Land. Chelonian Conservation and Biology 2(2): 223 – 229 Pauly D, Christensen V, Dalsgaard J, Froese R, Torres F (1998) Fishing down marine food webs. Science 279: 860–863 Polovina JJ, Howell E, Kobayashi DR, Seki MP (2001) The transition zone chlorophyll front, a dynamic global feature defining migration and forage habitat for marine resources. Progress in Oceanography 49: 469–483 Reilly JJ, Fedak MA (1991) Rates of water turnover and energy expenditure of free-living male common seals (Phoca vitulina). Journal of Zoology (London) 223: 431–468 Reina RD, Abernathy KJ, Marshall GJ, Spotila JR (2005) Respiratory frequency, dive behavior and social interactions of leatherback turtles, Dermochelys coriacea during the inter-nesting interval. Journal of Experimental Marine Biology and Ecology 316: 1-16  134  Rhodin AGJ (1985) Comparative chondro-osseous development and growth of marine turtles. Copeia 1985: 752-771 Saba VS, Shillinger GL, Swithenbank AM, Block BA, Spotila JR, Musick JA, Paladino FV (2008) An oceanographic context for the foraging ecology of eastern Pacific leatherback turtles: consequences of ENSO. Deep-Sea Research Part 1 55: 646-660 Southwood, A.L., Andrews, R.D., Paladino, F.V., Jones, D.R., 2005. Effects of swimming and diving behavior on body temperatures of Pacific leatherbacks in tropical seas. Physiol. Biochem. Zool. 78, 285–297 Southwood AL, Reina RD, Jones VS, Speakman JR, Jones DR (2006) Seasonal metabolism of juvenile green turtles (Chelonia mydas) at Heron Island, Australia. Canadian Journal of Zoology 84: 125–135 Sparling CE, Thompson D, Fedak MA, Gallon SM, Speakman JR (2008) Estimating the field metabolic rate of pinnipeds: doubly-labelled water gets the seal of approval. Functional Ecology 22: 245-254 Stewart K, Johnson C, Godfrey MH (2007) The minimum size of leatherbacks at reproductive maturity, with a review of sizes for nesting females from the Indian, Atlantic, and Pacific Ocean basins. Herpetological Journal 17: 123-128 Tomillo PS, Velez E, Reina RD, Piedra R, Paladino FV, Spotila JR (2007) Reassessment of the leatherback turtle (Dermochelys coriacea) nesting population at Parque Nacional Marino Las Baulas, Costa Rica: effects of conservation efforts. Chelonian Conservation and Biology 6: 54-62 Tringali MD (2006) A bayesian approach for the genetic tracking of cultured and released individuals. Fisheries Research 77(2): 159-172  135  Trullas S, Spotila JR, Paladino FV (2006) Energetics during hatchling dispersal of the olive ridley turtle Lepidochelys olivacea using doubly labeled water. Physiological Biochemical Zoology 79: 389–399 Wallace BP, Williams CL, Paladino FV, Morreale SJ, Lindstrom RT, Spotila JR (2005) Bioenergetics and diving activity of interesting leatherback turtles Dermochelys coriacea at Parque Nacional Marino Las Baulas, Costa Rica. Journal of Experimental Biology 208: 3873–3884 Wallace BP, Kilham SS, Paladino FV, Spotila JR (2006) Energy budget calculations indicate resource limitation in eastern Pacific leatherback turtles. Marine Ecology Progress Series 318: 263-270 Wallace BP and Jones TT (2008) What makes marine turtles go: A review of metabolic rates and their consequences. Journal Experimental Marine Biology and Ecology 356: 8-24 Witt MJ, Broderick AC, Johns DJ, Martin CS, Penrose R, Hoogmoed MS, Godley BJ (2007) Prey landscapes help identify potential foraging habitats for leatherback turtles in the northeast Atlantic. Marine Ecology Progress Series 337: 231–244 Zug GR, Parham JF (1996) Age and growth in leatherback turtles, Dermochelys coriacea (Testudines: Dermochelyidae): a skeletochronological analysis. Chelonian Conservation and Biology 2(2): 244-249 Zug GR, Kalb HJ, Luzar SJ (1997) Age and growth in wild Kemp‘s ridley seaturtles Lepidochelys kempii from skeletochronological data. Biological Conservation 80: 261-268  136  APPENDIX A  The University of British Columbia Animal Care Certificate Application Number:  A03-0255  Investigator or Course Director: David R. Jones Department:  Zoology  Animals Approved: Sea turtle 9  Start Date:  April 1, 1995  Approval Date: January 19, 2006  Funding Sources:  Funding Agency:  Natural Sciences and Engineering Research Council  Funding Title:  Validation of the Doubly Labelled Water Method in Green Sea Turtles, Chelonia mydas  Unfunded title:  N/A  137  Application Number: A04-0323 Investigator or Course Director: David R. Jones Department: Zoology Animals: Sea turtle 20  Start Date:  April 1, 1995  Approval Date:  December 8, 2006  Funding Sources: Funding Agency: Funding Title:  Natural Science Engineering Research Council Physiological adaptation of animals  Funding Agency:  Natural Science Engineering Research Council  Funding Title:  Ontogeny of Physiological Function in the Leatherback Sea Turtle (Dermochelys coriacea)  Unfunded title:  N/A  138  The Animal Care Committee has examined and approved the use of animals for the above experimental project. This certificate is valid for one year from the above start or approval date (whichever is later) provided there is no change in the experimental procedures. Annual review is required by the CCAC and some granting agencies.  A copy of this certificate must be displayed in your animal facility.  Office of Research Services and Administration 102, 6190 Agronomy Road, Vancouver, BC V6T 1Z3 Phone: 604-827-5111 Fax: 604-822-5093  139  

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