MOVEMENT PATTERNS, FORAGING ECOLOGY AND DIGESTIVE
PHYSIOLOGY OF BLACKTIP REEF SHARKS, CARCHARHINUS
MELANOPTERUS, AT PALMYRA ATOLL: A PREDATOR DOMINATED
ECOSYSTEM
A DISSERTATION SUBMITTED TO THE GRADUATE DIVISION OF THE UNIVERITY OF HAWAI‘I IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY
IN
ZOOLOGY
DECEMBER 2008
By Yannis P. Papastamatiou
Dissertation Committee:
Kim Holland, Chairperson Jeff Drazen Steve Karl Christopher Womersley Tadashi Fukami
UMI Number: 3349420
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DISSERTATION COMMITTEE
Chairperson
ii ACKNOWLEDGMENTS
I want to particularly thank my chair, Dr Kim Holland for taking me into his lab,
and for teaching me some of the tricks of the trade. I would like to thank Dr’s Jeff
Drazen, Steve Karl, Christopher Wormersley, and Tadashi Fukami for serving on my
committee and always being available for advice when I needed it.
I want to thank Dr Carl Meyer for providing me with a large number of
opportunities (and employment) while a graduate student. I would also like to thank Dr
Joanne Leong and Jane Ball for supporting me for a number of years and giving me the
freedom to go to remote locations and do research.
I would like to thank my colleagues Christopher Lowe, Alan Friedlander and Jenn
Caselle, without whom I would have never completed the work at Palmyra. For helping
me in the field in Palmyra I would like to thank T. Clark, N. Whitney, L. Davis, L. Max,
M. Sheehy, and G. Goodmanlowe. For all experiments with captive sharks I would like to thank my army of volunteers E. Aus, A. Stankus, T. Tinhan, M. Burns, J. Coloma, E.
Grau, C. Clarke, W. Connel, and J. Nakaya.
For advice, discussions and arguments I would like to thank my lab mates N.
Whitney, T. Clarke, D. Itano, J. Dale, T. Daly Engle, and M. Hutchinson. I would also like to acknowledge J. Dale and A. Taylor for statistical advice. L. Wedding and K.
Anthony designed the maps of Palmyra.
Finally I would like to thank Alexi and Caroline Papastamatiou, and Lori Davis for putting up with rather a lot from me, but still always being there when I needed it.
Most importantly, I would like to dedicate this dissertation in the memory of Dimitri
iii Papastamatiou for pushing me to do something I knew I wanted to do, but didn’t realize it at the time.
Funding was provided by the National Geographic Society (grant # 7990-06), a
UH Sea Grant Project Development fund, PADI Project Aware, and a grant from the Fish
Aggregating Devices as Instrumented Observatories of Pelagic Life (FADIO) under DG
Research of the European Commission. All experiments were approved by the
University of Hawaii Animal Care Committee.
iv ABSTRACT
Apex predators may have a strong regulatory function in marine ecosystems
through both density and behaviorally mediated effects. Understanding the ecological
impacts of apex predators is particularly important in predator dominated ecosystems
where intra-specific competition may be high. While a number of techniques are
available for quantifying predator movement patterns and distribution, little is known of
the causative factors that regulate these behaviors. One important aspect of predator
behavior is foraging, and an important regulating aspect of foraging is digestion. To advance our understanding of the interrelationship between gastric function and foraging
behavior, I tested two types of data loggers for deployment in shark stomachs. One type
of tag measured stomach acidity, the other the motility of the stomach wall. Both types
of tags were deployed in the stomachs of captive free-swimming blacktip reef sharks to
determine the effects of feeding and fasting on gastric digestive function. Gastric pH was
maintained low during long periods of fasting, suggesting continuous secretion of acid.
Gastric motility was higher for meals of mackerel than for similar sized meals of squid
with maximum motility occurring at meal sizes of 1 % body weight. Based on diel
patterns of gastric motility and pH, I predict that blacktips will feed daily and preferably
forage during times of low water temperature.
Palmyra Atoll is a remote, predator dominated ecosystem, and has a large
population of blacktip reef sharks. Blacktips at Palmyra are smaller than those at other
locations, which may be the result of food-limited growth due to intra-specific
competition. Palmyra consists of two lagoons (east and west), and abundance of sharks
appears to be similar in both lagoons. Active and passive tracking was used to study the
v movement patterns of the sharks at Palmyra. Sharks in the west lagoon utilized small
home ranges over scales of days to weeks. Adult sharks selected ledge habitats, while
smaller individuals selected sand-flats, and small pups were found in very shallow
waters. Fractal analysis revealed that sharks used patches that were 3 – 17 % of the
spatial scale of their home range, and that sharks move with a directed walk while in
patches but move randomly between patches.
Sharks in the west lagoon showed strong site fidelity with some individuals being
detected there for over 3 years. Sharks showed little movements between lagoons and
sharks in the east lagoon had shorter residence times. Sharks in the west lagoon had
higher body condition indices than those in the east lagoon and stable isotope analysis
revealed that trophic structure was different between the two lagoons. Conditions differ
between the two lagoons which may be driving differences in foraging success. This
study reveals the importance habitat can play in the movement patterns, home range and
foraging success of sharks and suggests that intra-specific competition could be a strong
regulator of apex predator populations in pristine predator dominated ecosystems.
Studies of the digestive system revealed that physiology may also regulate some aspects of movement patterns, although field studies will be required to test these hypotheses.
vi TABLE OF CONTENTS
ACKNOWLEDGEMENTS...... iii
ABSTRACT...... iv
TABLE OF CONTENTS...... vii
LIST OF TABLES...... ix
LIST OF FIGURES ...... 10
CHAPTER I: Introduction ...... 12
CHAPTER II: The response of gastric pH and motility to fasting and feeding in free swimming blacktip reef sharks, Carcharhinus melanopterus 21
ABSTRACT...... 21
INTRODUCTION ...... 22
MATERIALS AND METHODS...... 24
RESULTS ...... 31
DISCUSSION...... 44
CHAPTER III: A new acoustic pH transmitter for studying the feeding habits of free ranging sharks 53
ABSTRACT...... 53
INTRODUCTION ...... 53
MATERIALS AND METHODS...... 55
RESULTS ...... 59
DISCUSSION...... 59
CHAPTER IV: Scale dependent effects of habitat on movements and path structure of blacktip reef sharks,
vii Carcharhinus melanopterus, at Palmyra Atoll: a predator dominated ecosystem 67
ABSTRACT...... 67
INTRODUCTION ...... 68
MATERIALS AND METHODS...... 70
RESULTS ...... 81
DISCUSSION...... 97
CHAPTER V: Distribution, size frequency, and sex ratios of blacktip reef sharks, Carcharhinus melanopterus, at Palmyra Atoll: a predator dominated ecosystem...... 111
ABSTRACT...... 111
INTRODUCTION ...... 112
MATERIALS AND METHODS...... 113
RESULTS ...... 118
DISCUSSION...... 128
CHAPTER VI: Habitat influence residence time and foraging success of blacktip reef sharks at a predator dominated coral ecosystem...... 134
ABSTRACT………………………………………………...... 134
INTRODUCTION……………………………………………………...135
MATERIALS AND METHODS……………………………………….137
RESULTS………………………………………………………………144
DISCUSSION…………………………………………………………..160
Chapter VII: Conclusion………………………………………………………..168
REFERENCES ...... 170
viii LIST OF TABLES
Table Page
1. Summary information for blacktips used in experiments with pH and motility data-loggers...... 26
2. Multiple regression of square root transformed 7 h motility against stomach temperature, meal size, and meal size2 ...... 39
3. Summary information for sharks used in pH transmitter experiments……….60
4. Blacktip reef sharks actively tracked at Palmyra Atoll ...... 82
5. Home range and movement statistics for blacktip reef sharks actively tracked at Palmyra Atoll……………………………………………………....86
6. Summary of acoustic monitoring data for 9 blacktip reef sharks tagged in the west lagoon of Palmyra Atoll……………………………………………..96
7. Percentage detections by VR2 receivers at 8 locations throughout the Palmyra lagoons, for 6 acoustically tagged sharks……………………………98
8. Linear length-length relationships for blacktip reef sharks at Palmyra Atoll…………………………………………………………………………..123
9. CPUE and sex ratios for sharks caught on sand-flats within the west and east lagoons at Palmyra Atoll………………………………………………..125
10. Summary information for sharks tagged with acoustic transmitters in the Palmyra lagoons………………………………………………………..146
11. Percentage detections at individual receivers by blacktip reef sharks……..150
12. Null model pair-wise spatial overlap indices for acoustically tagged blacktip reef sharks…………………………………………………………154
ix LIST OF FIGURES
Figure Page
1. Continuous measurements of gastric pH and temperature in free swimming blacktip reef sharks ...... 32
2. Continuous measurements of gastric motility and temperature in free swimming blacktip reef sharks ...... 35
3. Examples of lag in motility following feeding in two free swimming blacktip reef sharks ...... 37
4. Effect of meal size on mean motility in blacktip reef sharks during the seven hours following consumption of mackerel, measured using a motility data-logger...... 40
5. Spectral analysis (FFT) of gastric motility data from blacktip reef sharks...... 42
6. FFT of gastric motility from sharks 2, 1, 3, and 4 ...... 45
7. An acoustic pH transmitter with transducer and pH electrode labeled...... 56
8. Continuous measurements of gastric pH and temperature in free-swimming blacktip reef sharks, as measured with a pH transmitter ...... 61
9. Linear regression analysis between meal size and area under the pH-time curve from blacktip reef sharks...... 63
10. Map of the Line Islands relative to the Central Pacific Ocean ...... 71
11. Home range of six blacktip reef sharks at Palmyra Atoll ...... 84
12. Habitat selection by blacktip reef sharks at Palmyra Atoll...... 88
13. Changes in VFractal with scale for movements of all blacktip reef sharks combined ...... 91
14. Fractal analysis of blacktip reef shark movement patterns at Palmyra Atoll .93
15. Seasonal movements of two blactip reef sharks from the west to the east lagoons ...... 99
16. Examples of long term movements of two acoustically tagged blacktip reef sharks ...... 101
10
17. Location of the Line Islands in the Central Pacific Ocean, and Palmyra’s location within the Line Island chain...... 114
18. Size frequency histogram for 254 blacktip reef sharks caught at Palmyra Atoll ...... 119
19. Length-weight regression for juvenile blacktip reef sharks at Palmyra Atoll ...... 121
20. Relationship between total length and clasper length for male blacktip reef sharks at Palmyra Atoll...... 126
21. Location of Palmyra Atoll within the Central Pacific Ocean and the location of VR2 receivers within the lagoons...... 138
22. Detection matrix for individual sharks by VR2’s throughout the Palmyra lagoons ...... 148
23. Non-metric multidimensional scaling ordination of spatial overlap between sharks...... 151
24. Scatter plots and FFT spectral analysis of shark movements at Palmyra Atoll ...... 155
25. Effect of shark total length on body condition indices and stable isotopes...158
11 Chapter I
Introduction
Apex predators may regulate ecosystems through top down control via density
and behaviorally mediated interactions between predator and prey species (Brown et al.,
1999, Dill et al., 2003, Preisser et al., 2005). Due to the low abundance and high mobility
of most apex predators, empirical data supporting theoretical predictions of predator
regulation are lacking, particularly marine species which also live in a concealing medium (e.g. Heithaus et al., 2002, Dill et al., 2003). Understanding the full ecological impacts of apex predators requires a comprehensive knowledge of the predator’s diet, movement patterns, distribution, density, population dynamics and life history characteristics. Furthermore, one of the main regulators of apex predator populations is thought to be intra-specific competition, which suggests that the ecological effects of the predators may be density dependent (e.g. Estes et al., 2003). It is therefore important to study the ecological impacts of predators under “base-line” conditions where predator population sizes have not been reduced by anthropogenic influences.
Sharks as apex predators
Many species of sharks are apex predators and may therefore be important regulators of marine ecosystems. However, empirical data validating these assumptions are particularly lacking, and the few studies that have investigated this issue suggest that there may be geographical and species specific differences in the extent of the shark’s importance in the ecosystem (e.g. Stevens et al., 2000, Bascompte et al., 2003, Dill et al.,
2003). Due to their life history characteristics, shark populations worldwide are declining
12 due to over-fishing making it even more crucial that we understand the role of sharks in
marine ecosystems because, if in fact their role as apex predators exerts a
disproportionate influence over the structure of ecosystems, their removal may have far
reaching and rapid impacts (Stevens et al., 2000, Myers and Worm 2003). Marine
Protected Areas (MPA’s) are one technique attracting much attention as a management tool to protect populations of marine apex predators (e.g. Chapman et al., 2005, Garla et al., 2006, Meyer et al., 2007). The efficient design of MPA’s requires understanding which habitats sharks select and how and why they behave in those habitats. There is considerable overlap in quantifying the ecological impacts of shark populations and using that information to design effective MPA’s.
Acoustic telemetry and its utilization in ecology
In the last couple of decades, there have been many advances in acoustic telemetry techniques that can be used to quantify space and habitat utilization of sharks over several spatial scales (e.g. Morrisey and Gruber 1993a, Holland et al., 1993, Weng et al., 2005, Garla et al., 2006). Active tracking involves attaching a small transmitter to
a shark, which emits a high frequency acoustic signal approximately every second that
can be detected by an underwater receiver attached to a boat or kayak, and which allows
the shark to be followed continuously. Active telemetry can quantify fish movements
and habitat utilization at high spatial resolution, as the researcher knows exactly where
the shark is while it is being tracked. However, tracking can only continue over a scale
of days, and therefore movements can only be quantified over short time scales (Morrisey
and Gruber 1993a, b, Holland et al., 1993). Passive tracking, where a network of
underwater listening stations detect transmitter equipped sharks when they come within
13 range of the various receivers, enables shark movements to be studied over scales of
years. However, it is not possible to know where the shark is when it is not being detected and so passive telemetry cannot measure habitat utilization with high spatial precision (e.g. Heupel et al., 2004, Garla et al., 2006). A combination of active and passive tracking can therefore quantify movements with both high spatial and temporal resolution.
Considerably harder, is being able to understand why animals select the habitats they use and why they exhibit certain behaviors when within those habitats. It is becoming increasingly clear that quantifying physiological processes in free ranging
animals is increasing in popularity and can partly help to explain some of the behaviors
seen in wild animals (Secor and Nagy 1994, Papastamatiou and Lowe 2004, Burns et al.,
2005).
Physiological measurements of free-ranging animals have been used to study
activity patterns, bioenergetics, feeding habits, and digestion of a wide range of animals
(Peters 1997a, Gremilliet et al., 2000, Lowe and Goldman 2001, Itoh et al., 2003). One
of the most important behavioral decisions an apex predator has to make is with regards to feeding, and foraging can partially explain the distribution patterns of many predators.
In endothermic marine predators, changes in stomach temperature can be used to quantify
feeding events (e.g. Gremilliet et al., 2000, Itoh et al., 2003). Unfortunately, stomach
temperature cannot be used to detect feeding events in ectothermic predators, and
currently we can only speculate as to when a top level fish predator is feeding, based on
changes in movement patterns. Conceptually, the physiology and constraints of the
digestive system can partially be used to explain an animals foraging behavior.
14 Furthermore, quantifying the conditions that lead to optimal digestion can improve
optimal foraging models and make predictions of the behavior of the animal in the wild.
Digestive physiology of sharks
Appetite in vertebrates is thought to be regulated by a variety of visceral and
systemic regulatory pathways, integrated with sensory information processed in the brain
(Mayer 1994, Sims et al., 1996). In elasmobranchs, there is an inverse relationship
between the evacuation of stomach contents and return of appetite and it has been
hypothesized that mechanoreceptors in the stomach wall respond to distention, and
initiate a graded nervous response leading to regulation of appetite (Sims et al., 1996).
Therefore, understanding the physiological regulation of the elasmobranch digestive
system will improve our understanding of shark feeding and associated behaviors in the
wild.
Many species of shark consume their prey whole, yet the morphology of the
intestine only allows the entrance of semi-liquid material (Andrews and Young 1993,
Motta 2004). Therefore the stomach is responsible for the complete breakdown of whole prey into semi-liquid chyme (Barrington 1942). Until recently, little was known about
gastric digestion in sharks other than species-specific measurements of gastric evacuation
rates. However, data-loggers have been used to quantify digestive processes in the
stomach of a couple of shark species (Papastamatiou and Lowe 2004, 2005,
Papastamatiou 2007). Prey digestion is accomplished by the secretion of concentrated
hydrochloric acid, protease enzymes, and contractions of the stomach wall (Holmgren
and Holberg 2005, Papastamatiou 2007). Shark species differ in their diet and feeding
habits, and therefore there should be inter-specific differences in the patterns of gastric
15 acid secretion and motility. For example, it has been noted that some species continually
secrete gastric acid when fasting, while others temporarily cease acid secretions
(Papastamatiou and Lowe 2004, 2005, Wood et al., 2007), which may be related to the feeding frequency of the species in the wild (Papastamatiou and Lowe 2005,
Papastamatiou 2007). Validating this will require studying the digestive system of a number of different species, and improving techniques used to quantify feeding behavior of wild sharks. In addition, measuring digestive variables in free-ranging sharks can be used as a proxy for feeding in the field. Specific changes in gastric pH occur every time a shark feeds; therefore continuous measurements of pH of sharks in the field will
indicate when the animal is actually ingesting prey (Papastamatiou and Lowe 2004,
2005). Finally, quantifying physiological processes of the stomach can be used to make prediction with regards to what behavior and habitats can optimize digestion. For example, how does the digestive system respond to changes in ambient temperature?
How does the stomach respond to different meal types and sizes? Should the species consume as much as possible when foraging, or is there an optimal meal size?
Blacktip reef sharks and Palmyra Atoll
The blacktip reef shark, Carcharhinus melanopterus, was chosen as a model species as it is a common and abundant shark on coral reefs, is large enough to be fitted
with data-loggers and transmitters, and is relatively easy to maintain in captivity.
Blacktip reef sharks are one of the most abundant shark species at many atolls and islands in the Pacific and Indian Ocean (Hobson 1963, Stevens 1983, Sandin et al., 2008).
Dietary studies in other locations show that blacktips are tertiary predators, and due to the
high population densities in many locations, could exert top-down control on coral
16 ecosystems (Stevens 1983, Cortes 1999). The only analysis of movement for this species
has been for individuals tracked over very short time periods (maximum 7 h), although
results suggest that blacktip reef sharks move over limited areas (Stevens 1983). Thus
they are an ideal study animal for both ecological and logistical reasons.
I had the opportunity to study blacktip reef sharks at Palmyra Atoll. Palmyra is
located in the central Pacific Ocean and has been a US National Wildlife Refuge since
2001. Before this time, Palmyra was largely uninhabited, although it was occupied by
the US Navy during World War 2. Dredging and construction substantially changed the
structure of the atoll, but the military left at the end of WW2, and the atoll was largely uninhabited from then until Palmyra’s designation as a refuge. As a consequence, the lack of anthropogenic influences has lead to a high abundance of apex predators, where sharks make up nearly 35 % of the fish biomass (Sandin et al., 2008, DeMartini et al.,
2008). Therefore, Palmyra is one of the few locations on earth where “base-line” numbers of apex predators can be found and subsequent intra-specific competition
studied. I therefore conducted a study of the digestive physiology, movement patterns
and foraging ecology of blacktip reef sharks at Palmyra Atoll.
Goals and objectives
The overall goal was to understand the ecological impacts of blacktip reef sharks
at Palmyra Atoll, a location where, because of their apparently high densities, sharks
should be under high levels of intra-specific competition, and the influence of these
predators on the reef ecosystem should be maximal. This was to be accomplished
through a detailed analysis of movement patterns and space utilization, foraging ecology
and trophic relationships, and basic life history characteristics. Furthermore, an
17 experimental study of gastric digestion in blacktips under captive conditions was conducted from which I could make predictions about shark behavior and how this could optimize digestion. Although I was not able to directly test these predictions, I was able to gather evidence and provide testable hypotheses on the effect digestive physiology may have on regulating shark behavior in the wild. The various components of the overall study are described in the following chapters
Chapter 2 describes an experimental study of gastric digestion in free-swimming captive black tip reef sharks. I fitted captive sharks with gastric data-loggers that measured gastric pH, motility and temperature. I determined how meal size and type influenced gastric digestion, and also quantified natural cycles in gastric digestion. Chapter 2 has been published as “Papastamatiou YP, KN Holland, SJ Purkis. 2007. The response of gastric pH and motility to fasting and feeding in free-swimming blacktip reef sharks,
Carcharhinus melanopterus. J. Exp. Mar. Biol. 345: 129-140”
Chapter 3 describes the testing of a custom designed pH acoustic transmitter. The transmitter was tested with captive blacktip reef sharks, and sharks were fed meals of fish at different sizes. The goal was to determine if a pH transmitter could quantify feeding frequency and meal size in free-ranging blacktip reef sharks. Chapter 3 has been published as “Papastamatiou YP, CG Meyer, KN Holland. 2008. A new acoustic pH transmitter for studying the feeding habits of sharks. Aquat. Liv. Res. 20(4): 287-290”
18 Chapter 4 describes “Scale dependent effects of habitat on movements and foraging strategies of blacktip reef sharks at Palmyra Atoll”. I used active telemetry to measure short term movements, behavior and habitat utilization of blacktip reef sharks at Palmyra
Atoll. Chapter 4 has been accepted for publication in Ecology.
Chapter 5 describes “Distribution, size frequency, and sex ratios of blacktip reef sharks at
Palmyra atoll”. I fished for sharks throughout the Palmyra lagoons to describe the distribution of sharks, and how sex ratios may vary spatially. I also quantified the size frequency of sharks at the atoll and determined size of maturity of male sharks. Chapter
5 is currently in review in journal of Fish Biology.
The final chapter, Chapter 6, describes “Habitat influences residence time and foraging success of blacktip reef sharks at Palmyra Atoll”. I used passive telemetry to look at movements of sharks between two lagoons over a time scale of several years. I also used stable isotopes and a body condition index to determine if trophic structure and foraging success differed for sharks between the two lagoons. My goal was to correlate movements and behavior with foraging success.
Collectively, I made prediction of shark foraging behavior based on the physiology of the digestive system. I obtained evidence that some of these predictions may be true, although I was not able to verify them. I also quantified the movements of a population of sharks with both high spatial and temporal resolution, and provide the first
19 evidence showing that differences in habitat use and movements can affect foraging success.
20 Chapter II
The response of gastric pH and motility to fasting and feeding in free swimming
blacktip reef sharks, Carcharhinus melanopterus
ABSTRACT
In many fish and reptiles, gastric digestion is responsible for the complete breakdown of prey items into semi-liquid chyme. The responses of the stomach to feeding and to periods of fasting are, however, unknown for many lower vertebrates. We inserted data loggers into the stomachs of free-swimming captive adult blacktip reef sharks (Carcharhinus melanopterus) to quantify gastric pH, motility and temperature during fasting and following ingestion of food. Gastric acid secretion was continuous, even during long periods of fasting, with a mean pH of 1.41 ± 0.40 (± 1SD) when the stomach was empty. Stomach contractions were greater following meals of mackerel than for those of squid. Gastric motility following feeding on mackerel, was positively influenced by ambient temperature, and followed a quadratic relationship with meal size, with maximum motility occurring after meals of 0.8 - 1.0 % body weight. Diel changes in gastric motility were apparent, and were most likely caused by diel changes in ambient temperature. Gastric digestion in blacktip reef sharks is affected by both biotic and abiotic variables. We hypothesize that behavioral strategies adopted by sharks in the field may be an attempt to optimize digestion by selecting for appropriate environmental conditions.
21 INTRODUCTION
Gastric digestion in carnivorous vertebrates is responsible for the breakdown of ingested prey items into semi-liquid chyme. The role of the stomach is particularly important in lower vertebrates such as fish and reptiles, many of which ingest their prey whole with little mastication (e.g., Secor 2003, Motta 2004). Two components to gastric digestion occur: chemical digestion accomplished by the secretion of concentrated hydrochloric acid (HCl) and digestive enzymes, and mechanical digestion accomplished by muscular contractions of the stomach wall (Mayer 1994, Holmgren and Holmberg
2005).
Elasmobranch fishes are one of the first group of carnivorous vertebrates to have evolved a functional stomach and (based on the identification of H+-K+ ATPase in acid
secreting cells) probably one of the first to have evolved an acid secreting stomach
(Smolka et al., 1994). In addition, the morphology of the stomach permits only the
passage of semi-liquid chyme into the intestine, yet many species of elasmobranch ingest
their prey whole, highlighting the importance of the stomach to food breakdown
(Andrews and Young 1993, Motta 2004).
Elasmobranchs are capable of secreting highly acidic gastric fluids (down to pH
0.4, Papastamatiou and Lowe 2004, 2005). Distention of the stomach wall as food enters
is the initial stimulus for increased acid secretion (Smit 1967), followed by the action of
secretagogues such as gastrin and histamine, although the interactions between hormones
and acid secretion are not well known (Hogben 1967, Vigna 1983). There are inter-
specific differences among elasmobranchs in the response of gastric acid secretion to
fasting, with some species continuously secreting acid while others periodically cease
22 secretions during fasting (Barrington 1942, Papastamatiou and Lowe 2004, 2005).
Secreted HCl aids in the physico-chemical breakdown of the hard parts of prey and
contributes to enzymatic digestion by converting the inactive zymogen pepsinogen into
the proteolytic enzyme pepsin (Guerard and Le Gal 1987, Holmgren and Nilsson 1999).
Some elasmobranchs are also capable of secreting chitinase enzymes, which also have
optimal function at low pH, and break down chitin-containing exoskeletons (Fange et al.,
1979).
To date, gastric motility has only been measured in euthanized or anaesthetized elasmobranchs, although results suggest that gastric motility is under the control of both nervous and hormonal mechanisms (Andrews and Young 1993, Holmgren and Nilsson
1999, Buddington and Krogdahl 2004). A variety of neurotransmitters have been identified in elasmobranch gut neurons (Nilsson and Holmgren 1988) and it appears that there is both nervous inhibition and excitation of the stomach muscles (Campbell 1975,
Andrews and Young 1993). Elasmobranchs are known to have relatively slow gastric evacuation rates (Wetherbee et al., 1990), and electrical stimulation of the splanchnic nerve in lesser spotted dogfish, Scyliorhinus canicula, induced gastric contractions but peristalsis did not move the stomach contents into the small intestine (Andrews and
Young 1993). Presently, it remains unclear whether gastric motility in elasmobranchs only functions to mix food items and to pass chyme out of the stomach, or if motility is also involved in mechanical trituration. Gastric evacuation rates (and presumably motility) in elasmobranchs are influenced by a variety of factors including: meal size, surface area of ingested prey, prey lipid composition, the presence of skeletal or chitin containing hard-parts, and feeding periodicity (Wetherbee et al., 1990, Schurdak and
23 Gruber 1989). In summary, it is probable that species specific differences in stomach motility and patterns of acid secretion are shaped by species specific diet and feeding strategies.
Presently, very little is known of the response of gastric acid secretion and motility following feeding and during fasting in free-swimming elasmobranchs.
Obtaining such data under semi-natural conditions is important as it enables the physiological response of the stomach to be put into an ecological context, and subsequently applied to the study of the feeding strategy and optimal foraging behavior of the animal in the wild. Our goals were to quantify changes in gastric pH, temperature and motility in a captive free-swimming elasmobranch, the blacktip reef shark
(Carcharhinus melanopterus), using autonomous data-loggers under semi-natural conditions. The blacktip reef shark was chosen as a model species because it is an abundant predator on coral reefs in tropical and semitropical regions of the Pacific and
Indian Oceans (Compagno et al., 2005), is large enough to retain gastric data-loggers, and feeds and behaves normally while in captivity. Our specific objectives were to: (1) determine the post-prandial changes in gastric pH and motility in free-swimming captive blacktip reef sharks; (2) quantify the influence of meal size, meal type, and temperature on gastric motility; (3) determine the response of pH and motility during periods of fasting; and (4) determine if there were any diel changes in the profiles of gastric pH and motility. Because many species of shark are considered nocturnal foragers (Wetherbee et al., 1990), we hypothesize that diel differences in gastric digestion will occur.
MATERIALS AND METHODS
24 Study animals
Tests were conducted with five captive adult blacktip reef sharks (Carcharhinus
melanopterus, Quoy & Gaimard 1824), total length 145.6 ± 6.8 cm (mean ± 1 SD) and
mass 21.8 ± 3.1 kg (Table 1). All sharks were maintained at the Hawaii Institute of
Marine Biology in a sectioned off lagoon (120 x 20 m) consisting of coral rubble, coral, and sand, with a maximum depth of 3 m. The lagoon is tidally flushed and contains a
fish and invertebrate community typical of Kaneohe bay, Oahu, Hawaii. Prior to testing,
sharks were fed to satiation two to three times a week with mackerel (Scomber spp.).
Animals used in experiments were moved into a smaller rectangular section
(approximately 10 x 20 m), with similar habitat characteristics as the rest of the lagoon.
No more than two sharks were maintained in the testing area at any one time. Sharks were acclimated to the test area until they resumed feeding, after which they were fasted for a week before the experiments began. Sharks were fitted with one of two types of data-logger measuring either stomach pH or gastric motility.
Gastric pH
To measure gastric pH and temperature in free-swimming blacktip reef sharks, we used autonomous pH/temperature data-loggers (earth & Ocean Technologies, Kiel
Germany). The data-loggers are cylindrical (11 x 2 cm), weigh approximately 80 g in air and consist of a pH micro-glass electrode, a reference electrode with a free-diffusion liquid junction and a 12-bit data-logger encased in a titanium shell (Peters 1997 a, b).
The reference electrodes are designed to compensate for any pressure changes associated
25 Table 1 Summary information for adult blacktip reef sharks (Carcharhinus melanopterus) used in experiments with pH and motility data-loggers.
Shark # TL Mass Sex Min Max Mean (cm) (kg) pH pH pH 1 139 19 F 1.2 3.6 1.7 2 144 21 F 0.8 3.4 2.0 3 140 19 M 0.4 5.3 2.0 4 150 24 F 1.2 4.0 1.9 5 155 26 F - - -
26 with diving (Peters 1997b). A sensor on the data-logger also measured temperature
(resolution of 0.1 ºC). Before deployment, the pH data-loggers were programmed to
record pH and temperature every 30 sec and were calibrated in NBS standard pH buffers
(1.68, 4.01, 6.86, and 10.01).
To deploy the pH data-loggers, we netted a shark and inverted it in a stretcher to induce tonic immobility (see Papastamatiou and Lowe 2005). Additional anesthesia was induced by inserting a 2 cm diameter siphon into the mouth and applying a solution of
MS 222 (0.15 g l-1) to the gills. It took between 5 – 10 min before the shark was
anaesthetized to a level of immobility, after which we inserted a lubricated 3 cm diameter
PVC pipe through the mouth into the stomach. The pH data-logger was dropped down
the pipe with the pH sensor pointing towards the caudal fin (i.e. at the base of the cardiac
portion of the stomach), followed by pieces of bait fish to prevent premature regurgitation
of the data-logger. We then removed the pipe and measured and sexed the shark before
reviving it by manually ‘swimming’ the animal through the lagoon water. Each shark
revived within approximately 10-15 min, after which it was observed for an additional 15
min to ensure normal swimming behavior. We determined shark mass using the length-
weight regression Weight= 1.004 * 10-6(Total length)3.39 (Stevens, 1984). During the
period that the pH data-logger was retained in the stomach, we fed each shark meals of
mackerel (Scomber spp.) at a variety of ration sizes (Table 1). Two sharks were also fed
meals of reef fish (various Acanthurus and Chaetodon species), and one shark was also
fasted for 12 d. The data logger was deployed in each shark only once.
The pH data-loggers can record accurate pH data for up to 16 d, depending on
electrolyte outflow rate (see Peters 1997b). If the shark had not regurgitated the data-
27 logger within 16 d, then we restrained the shark as described above and used a magnetic device to remove it from the stomach. After retrieval, the data-loggers were re-calibrated with the same NBS standard pH buffers used prior to deployment. The data were then downloaded and analyzed using pHG 2.0 software (Jensen Software Systems), which interpolates and corrects pH data for any drift of the electrode and also for changes in stomach temperature (Peters 1997a). Error analysis of pH electrode performance was determined using the pH drift model described by Peters (1997a).
We determined titration time for each meal that each shark consumed, with titration time defined as the time taken for pH to return to 2.0 (baseline) (Gardner et al.,
2002). To determine the time of onset of a response, we first established a baseline by analyzing gastric acidity in the two hours prior to feeding. This period was divided into
10 min blocks and onset of a response (P1) was defined as the first of two consecutive 10
min intervals where pH was < 2.0 for only 5 % of the time. We analyzed the 24 h period
following feeding in the same way and defined the end of the response (P2) as the first of
two consecutive 10 min intervals where pH was > 2.0 for less than 10 % of the time.
Titration time was calculated as P2 - P1. We used linear regression analysis to quantify
the relationship between meal size and titration time. For each meal, we also measured
the area under the pH curve using ArcView GIS (ver 3.2). A linear regression was used
to compare meal size to area under the pH - elapsed time curve.
Gastric motility
We measured gastric motility using a motility/temperature data-logger (14 x 1.9
cm, length x diameter, 45 g in air, earth & Ocean Technologies, Kiel Germany). The
28 sensor consists of a piezoelectric film encased in a flexible silicon bulb, connected to an
8-bit data-logger. Movement of the piezoelectric film generates a voltage, the size of
which is a function of the extent and speed of deflection (Peters 2004). The motility sensor provides a cumulative measure of stomach muscle activity over time. In our case, the data-logger was programmed to record stomach motility every 15 sec. A temperature sensor coupled to the data-logger also enabled simultaneous measurements of stomach temperature (resolution 0.1 ºC).
The stomach motility and temperature (SMT) data-loggers were deployed as described above for the pH data-loggers. However, the former were deployed with the sensor pointing towards the mouth. To evaluate any spatial differences in gastric motility within the stomach, one shark had the SMT data-logger deployed with the motility sensor pointing towards the caudal fin. During deployment, sharks were fed squid (Loligo spp.)
or mackerel (Scomber spp.) at a variety of ration sizes. SMT data-loggers were either
regurgitated by the shark or we retrieved them as described. After retrieval, data from the
SMT data-loggers were downloaded and analyzed using pHG 2.0 software (Jensen
Software System).
We used a General Linear Model (GLM) to evaluate the effects of temperature,
meals size and meal type on gastric motility. In all cases, motility was the dependent
variable while meal size, temperature, and meal type were covariates. Meal size and
temperature were also set as interactive variables. Two measures of motility were used:
(1) the mean over the first 7 h after feeding, and (2) the mean over the first 24 h after
feeding. We used 7 h in addition to 24 h because there appeared to be an approximate 7 h
delay between feeding and the onset of the strongest contractions and we wanted to test if
29 there were differences in motility related to feeding during the 7 h “lag” period (see
results). Because the GLM showed that motility differed between mackerel and squid,
we used multiple regression analysis for mackerel and squid meals separately. Motility
values were not normally distributed, so we applied a square root transformation. The
effect of meal size on motility appeared to be best described by a quadratic equation, so
meal size was squared. In all cases, the residuals from the GLM and regressions were
examined to ensure that all assumptions of the models were met. All GLM and multiple
regression analysis were performed using Minitab (ver. 14).
Due to the logistics associated with maintaining large adult sharks in captivity, we
only had five sharks with which to deploy data-loggers (Table 1). As a consequence, we
deployed the motility logger in each shark on two separate occasions (with the exception
of shark # 2, in which it was only deployed once). Although this constitutes a degree of pseudo-replication, we visually checked the distribution of all data points to ensure that statistical analyses were not strongly influenced by data from one individual (by examining maximum and minimum data points).
We used time series analysis to determine if there were any cyclical patterns in gastric motility, applying a Fast Fourier Transformation (FFT) which converts time-series data into frequencies, thereby facilitating the identification of temporal periodicity in the dataset. The FFT produces a power spectrum with the power of each frequency being dependent on how well the data fit the sinusoidal wave of that particular frequency
(Chatfield 1996). The time period of the event could then be calculated as the inverse of frequency, with each block of data equivalent to 15 sec (the sampling rate of the data- logger). For example, there are 5760 data blocks (each equivalent to 15 sec) in a 24 h
30 period, which translates to a frequency of 0.00017. All motility data were smoothed
using a Hamming window before running the FFT (Chatfield, 1996). FFT analysis was
performed using Statistica (ver.7).
RESULTS
Gastric pH
Drift of the pH electrodes were generally low, with resolution varying between
0.004 - 0.06 pH units and error between 0.02 – 0.4 (Table 1). Regardless, the blacktip reef sharks maintained an acidic stomach at all times (maximum pH: 5.3, Fig.1, Table 1).
During periods of fasting (> 48 h after feeding), gastric pH was 1.41 ± 0.40 (mean ±
1SD). In all sharks, feeding caused a rapid increase in gastric pH (decrease in acidity) of
1.66 ± 0.41 units to a peak value of 3.15 ± 0.41, followed by a gradual decrease back down to baseline (more acidic) levels. The rate of increase in pH following feeding
(0.027 ± 0.019 pH units/min) was faster than the subsequent decrease (0.0015 ± 0.0005 pH units/min, t test paired sample for means, t = 3.77, p = 0.007). There was no significant effect of meal size on titration time (p = 0.26, F = 1.93) or area under the pH/time curve (p = 0.34, F = 1.25). However, the regression was strongly influenced by one outlier point. When this point was removed, meal size (expressed in g) affected both titration time (p = 0.04, F = 21.7, r2 = 0.87) and area (p = 0.04, F = 36.0, r2 = 0.92).
Shark #3 was fasted for 12 d and showed pH profiles with two separate phases
(Fig. 1c). For the first seven days of fasting, pH remained relatively stable between 1.4 and 2.1, but after day seven, pH started to fluctuate between 5.3 and 0.4 even though no
31 Figure 1. Continuous measurements of gastric pH and temperature in free swimming blacktip reef sharks (Carcharhinus melanopterus). Lower line is gastric pH, upper line is gastric temperature. Arrows indicate time of feeding, and the number above each arrow represents meal size expressed as % BW. “?” indicates that a meal of unknown size was consumed. Meal codes are “M” for mackerel (Scomber spp.), “RF” for reef fish
(Acanthurus, Chaetodon), and “S” for squid (Loligo spp). Data from individual sharks are shown in separate panels: (a) shark #1, (b) shark #2, (c) shark #3 (fasted for entire duration of deployment), (d) shark #4.
32 a b 10 30 10 30
25 25 8 8
20 20 6 0.5 M 6 1.1 M 1.1 RF 1.35 RF ? 15
pH 15 pH 4 4 10 10 Temperature (C) Temperature (C) 2 2 5 5
0 0 0 0 012345 02468
Time (d) Time (d)
cd 10 30 10 30
25 25 8 8
20 20 6 6 1.3 M 0.88 M 0.88 S 0.3 M 15
15 pH pH 4 4 10 10 Temperature (C) Temperature (C) 2 2 5 5
0 0 0 0 024681012 0246810
Time (d) Time (d)
33 feeding occurred. FFT analysis was used to analyze both these phases in shark #3. As expected, no major peaks in the density spectrum were observed during the first phase
(when pH remained constant). The second phase produced two peaks however; one at
27.8 h and one at 41.7 h. We interpret this as a diel fluctuation in pH, with pH being lowest between 0600 – 0800 in the morning and highest during the late afternoon.
Gastric motility
Motility appeared to be reduced during the first two days of deployment and consequently motility data were only used from meals given to sharks > 2 d after deployment of the logger (Fig.2). All sharks showed a delay of 7 – 12 h following feeding, before the onset of strong contractions (Fig. 3). The results of the GLM showed that both meal type (F=13.79, p=0.006) and stomach temperature (F=6.44, p=0.035) affected motility during the 7 h post-prandial period, while only meal type (F=9.16, p=0.019) affected motility during the first 24 h post-feeding. Meals of mackerel elicited stronger contractions (0.60 ± 0.37 relative units) than meals of squid (0.21 ± 0.07 relative units, F=13.79, p=0.006). Multiple regression analysis for mackerel meals showed that meal size2, and stomach temperature affected motility during the 7 h following feeding
(r2=82.3, F=10.31, p=0.043, Table 2), but not over the 24 h following feeding (F=3.95, p=0.145). The effect of meal size on 7 h post-prandial motility was best described by a quadratic equation (r2=0.53, Fig. 4). Temperature and meal size did not affect motility during the first 7 or 24 h following consumption of squid meals (F=0.68, p=0.572).
The FFT spectra showed a motility peak for all sharks at a frequency of approximately 0.0002, regardless of whether the shark ate during that time period (Fig.5).
34 Figure 2. Continuous measurements of gastric motility and stomach temperature in free
swimming black tip reef sharks (Carcharhinus melanopterus). The upper trace shows
stomach temperature. Data from individual sharks are shown in separate panels: (a)
Shark #5 (In this instance the data logger was deployed with sensor pointing towards
caudal fin whereas all other sharks had sensor deployed pointing towards mouth.), (b)
shark #4, (c) shark #3 (fasted for entire deployment), (d) shark #2 (The gap in data set
was due to data-logger failure). Arrows indicate time of feeding, and number above
arrow represents meal size expressed as a percentage of body mass. “?” indicates a meal
of unknown size was consumed. Meals codes are “M” for mackerel (Scomber spp.), and
“S” for squid (Loligo spp.). High contractions towards end of deployment seen in panel’s c, and d are most likely attempts to regurgitate data-logger.
35 a b 100 30 300 30
25 250 25 80 0.8 M
20 200 20 60 1.0 M 15 1 M 150 0.4 M 15 40 10 100 10 Temperature (C) 20 Temperature (C) Motility (relative units) 5 Motility (relative units) 50 5
0 0 0 0 1234 012345 Time (d) Time (d)
c d
300 30 300 30
250 25 250 25
200 20 200 1 S 20
150 15 150 ? S 15 1 M 100 10 100 10 Temperature (C) Temperature (C) Motility (relative units) (relative Motility Motility (relative units) 50 5 50 5
0 0 0 0 02468 024 101214 Time (d) Time (d)
36 Figure 3. Examples of lag in motility following feeding in two free swimming blacktip
reef sharks (Carcharhinus melanopterus). Upper line in each graph is stomach
temperature. Results from sharks #2 and #4 show raw data (a, c) and running average (b, d). “F” indicates time of feeding, whereas “C” shows time of strong contractions.
37 a b 120 30 2
100 25
80 20
C 60 F 15 1
40 10 Temperature (C)
Motility (relative units) 20 5 Mean motility (relative units) (relative motility Mean 0 0 0 051015 0102030 c Time (h) d Time (h) 120 30 2 F 100 25
80 20
60 C 15 1
40 shark 5 10 Temperature (C)
Motility (relative units) 20 5 Mean motility (relative units) 0 0 0 0 5 10 15 20 25 30 0 102030 Time (h) Time (h)
38 Table 2. Multiple regression of square root transformed seven hour motility against stomach temperature, meal size and meal size2. Data are from blacktip reef sharks fed meals of mackerel (Scomber spp.).
Predictor Coef SE Coef T P
Constant -0.9409 0.5684 -1.66 0.196
Meal size -0.0676 0.1197 -0.56 0.612
Temp 0.0719 0.0219 3.29 0.046
Meal size2 -1.3468 0.3429 -3.93 0.029
S=0.105 R2= 91.2% R2 (adj.)=82.3%
39 Figure 4. Effect of meal size on mean motility in blacktip reef sharks (Carcharhinus melanopterus) during the seven hours following consumption of mackerel (Scomber spp.), measured using an motility data-logger. The solid line is a curve fitted using a quadratic equation (r2= 0.53, p= 0.029). Maximum motility occurs after sharks consume meals of 0.8-1.0 % of body weight.
40 1.4
1.2
1.0
0.8
0.6
0.4
0.2 7 h Motility (relative units)
0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 Meal size (%BW)
41 Figure 5. Spectral analysis (FFT) of gastric motility data from blacktip reef sharks. Data are from sharks # 1 - 5 (panels a - e respectively). All sharks had the motility sensor pointing towards the mouth, except for shark #5 (e) which had the sensor pointing towards the caudal fin. Different scales were used on the y-axis to clarify data presentation.
42
a b 1400 1600
1200 1400 1200 1000 1000 800 800 600 600 Density Density 400 400 200 200 0 0 0 1020304050 0 1020304050 Period (h) Period (h) c d 800 1200 700 1000
600 800 500 600 400 Density
Density 400 300 200 200 100 0 0 1020304050 0 1020304050 Period (h) e Period (h) 180 160 140 120 100 80
Density 60 40 20 0 0 1020304050 Period (h)
43 This frequency translates to a time period of 23.4 ± 1.8 h. Shark #5 (the one animal that
had the sensor pointing towards the caudal fin) did not show any peaks in the frequency
spectra (Fig. 5e). All sharks that fed showed a second peak in motility with a period of
2.0 ± 0.3 h, but this peak was absent from sharks that were fasted (Fig. 6).
DISCUSSION
The use of intra-lumenal data-loggers to measure digestive variables appears to be a viable technique in medium to large sized sharks. Although we did not quantify the effects of the data-logger on acid secretion in blacktip reef sharks, previous work with leopard sharks (Triakis semifasciata) showed no effect of the data-loggers on gastric acid secretion (Papastamatiou and Lowe 2004). All blacktip reef sharks behaved similarly to non-instrumented sharks, and resumed feeding within one day of deployment.
Gastric pH
Blacktip reef sharks are capable of secreting highly acidic gastric fluid (minimum measured pH 0.4). Gastric acid secretion appears to be continuous in this species because low pH values were recorded even after long periods of fasting (e.g. shark #3 was fasted for 12 d, see fig.1c). Maximum pH recorded for any blacktip was 5.3, and pH remained at this level for only a short period of time before returning to low levels. It has been proposed that shark species that feed frequently in the wild continuously secrete gastric acid during fasting thereby enabling them to be in a state of physiological readiness for the next meal, whereas sharks which feed less frequently (e.g. nurse sharks,
Ginglymostoma cirratum) may periodically cease acid secretion while the stomach is empty as an energy conserving technique (Papastamatiou and Lowe 2004, 2005,
44 Figure 6. FFT of gastric motility data from shark #2 (a), #1 (b), #3 (c), and #4 (d).
Sharks #2 and #1 (a,b) were fed during deployment of the data-logger, while sharks #3 and #4 (c,d) were fasted. The arrow indicates peaks in the gastric motility spectrum equivalent to 2.0 ± 0.3 h in fed sharks. Different scales were used on the y-axis in panel d to clarify data presentation.
45 a c 5e+7 5e+7
4e+7 4e+7
3e+7 3e+7 Power Power 2e+7 2e+7
1e+7 1e+7
0 0 1.5 2.0 2.5 3.0 3.5 1.5 2.0 2.5 3.0 3.5 Log period (min) Log period (min) b d 5e+7 6e+6
4e+7 5e+6 4e+6 3e+7 3e+6 Power Power 2e+7 2e+6
1e+7 1e+6
0 0 1.5 2.0 2.5 3.0 3.5 1.5 2.0 2.5 3.0 3.5 Log period (min) Log period (min)
46 Papastamatiou 2007). Although little is known about the feeding habits of blacktip reef
sharks in the wild, they are an active continuously swimming species that lives in semi- tropical and tropical waters, and spend a considerable amount of time searching over sand flats and along reef ledges (Papastamatiou and Lowe unpublished, Stevens 1984). In combination, these factors suggest that blacktip reef sharks probably have high energy requirements and may have to feed frequently. If this is the case, the present result
appears to agree with the hypothesis that feeding frequency influences gastric acid
secretion patterns in sharks.
Following feeding, a rapid increase in gastric pH occurred with a subsequent
gradual decrease back to baseline levels. We interpret the rapid increase in pH as being
caused by seawater and the food items themselves (most of which are alkaline) entering
the stomach and diluting or buffering the small amounts of gastric fluids that are present
in the stomach. After feeding, an increase in gastric acid secretion is presumably
triggered by stomach distention and the action of secretagogues such as histamine and
gastrin (e.g. Smit 1967, Hogben 1967, Vigna 1983) resulting in re-acidification of the
stomach. This interpretation is supported by the fact that the amount of time taken for the
stomach to re-acidify appears to be a function of meal size.
Gastric pepsin and chitinase enzymes have optimum activity at low pH. For
example, pepsin from the lesser spotted dogfish (Scyliorhinus canicula) has an optimum
pH of 2.5 (Guerard and Le Gal 1987), while chitinase enzymes from several species of
shark and skate show optimal activity at pH of approximately 1.6 (Fange et al., 1979).
Although gastric enzymes have not been identified in blacktip reef sharks, it is highly
likely that at least one, if not both, of these enzymes are present and the observed gastric
47 conditions would be optimal for both enzymes (especially in the 12 - 24 h following
feeding).
Gastric motility
In all blacktip reef sharks there appeared to be a delay in heightened stomach activity of 7 -12 h following feeding. A delay in active gastric contractions following feeding has also been seen in teleosts such as bluefin tuna, Thunnus thynnus, and rainbow
trout, Oncorhynchus mykiss (Carey et al., 1984, Olsson et al., 1999). The delay in active
contractions following feeding (also known as gastric accommodation or relaxation), is
the initial response following distention of the stomach wall, and is thought to allow for
more space in the stomach and for the accumulation of gastric fluids before mixing
(Mayer 1994, Holmgren and Holmberg 2005). The delay in motility observed in the
current experiment may be related to the post-prandial lag in gastric evacuation of
stomach contents observed in other elasmobranchs such as juvenile sandbar sharks,
Carcharhinus plumbeus (Medved 1985), and scalloped hammerhead sharks, Sphryna
lewini (Bush and Holland 2002).
The results from the present study show that gastric motility is a function of
abiotic and biotic variables. Meals of squid elicited lower levels of stomach contraction
than similar sized meals of mackerel (Scomber spp.), which is contrary to predictions based on data from other vertebrates. Stomach motility in vertebrates is though to be sensitive to lipid levels, with high-lipid prey taking longer to evacuate from the stomach than low-lipid prey (Anderson 2001, Mayer 1994). The mackerel used in the present study have higher lipid levels than those found in squid (e.g. Mackerel is 4.72 % lipids, as
opposed to squid which is 1.72 %, O’Neal Scientific services inc., MO) and should have
48 elicited weaker contractions than squid. These two food items also differ in their
physical digestibility however. Squid contains collagen fibers which increase the tissues
resistance to digestive action (Jackson et al., 1987). Our results agree with studies of
gastric evacuation rates in elasmobranchs. Gastric evacuation rates for little skate (Raja
erinacea) fed meals of squid were slower than those fed high lipid sand lance, or lipid
poor krill (Nelson and Ross, 1995), while blue sharks (Prionace glauca) took longer to evacuate squid than they did anchovies (Tricas 1977). The reduced motility after squid meals increases the time of exposure of squid tissue to HCl and gastric enzymes, required for the break down of collagen fibers (Jackson et al., 1987).
We also found that stomach temperature correlated with increased gastric contractions for meals of mackerel, but not squid. It is well established that gastric evacuation in fish is positively influenced by temperature (e.g. Nelson and Ross 1995,
Bush and Holland 2002), but it is unclear why temperature did not influence gastric motility patterns for squid meals in blacktip reef sharks, although the small sample size may have compromised our results. The movement patterns of some elasmobranchs in the field may be for behavioral thermoregulation (e.g. Carey and Scharold 1990, Matern et al., 2000). Moving into warmer water should increase gastric evacuation rates
(theoretically lowering digestive efficiency), but our results suggest that this deficit may be countered by improved mixing of stomach contents.
The magnitude of gastric contractions during the 7 h following feeding on mackerel was best modeled to meal size using a quadratic equation. Gastric motility increased with meal size until the sharks were consuming 0.8-1.0 % of their body weight
(BW), after which there was a decline in motility. It is thought that gastric motility in
49 vertebrates increases as a function of distention of the stomach wall (Mayer 1994).
Although this has never been explicitly tested in fish, preliminary results from dab
(Limnada limnada) suggest that gastric motility is a function of the cube root of the size
of stomach contents (Jobling 1974). Previous studies have shown that increased meal
size also increases gastric evacuation time in elasmobranchs (Sims et al., 1996, Bush and
Holland 2002). In lemon sharks (Negaprion brevirostris), initial processing of prey occurred faster when meal size increased, but total gut transit time also increased, suggesting that the rate of digestion remained constant (Wetherbee and Gruber 1990).
Our results agree with those of Wetherbee and Gruber (1990) because motility during the
7 h following ingestion increased with meal size but total motility during the 24 h following feeding did not.
Based on our results, we hypothesize that optimum gastric digestive efficiency in blacktip reef sharks occurs when meal size is 0.8-1.0 % of BW. Daily ration has not been measured in blacktip reef sharks, but for other carcharhinid sharks it has been calculated as approximately 1 – 2 % of BW day-1 (Wetherbee et al., 1990). The observed decrease
in gastric motility at high ration levels may be due to stomach fullness reducing stomach
contractions and consequently mixing. Gross conversion efficiency (the efficiency by
which ingested prey items are converted into predator tissue) in elasmobranchs is thought
to decrease at high ration levels (Cortes and Gruber 1994, Duncan 2006). Optimal gross
conversion efficiency was achieved at relatively high ration levels (e.g. 5.1 % BW/ per
day for scalloped hammerhead sharks, Duncan 2006), but those studies were conducted
using juvenile animals with higher mass specific metabolic rates than adults. We found
that a large proportion of the variability in gastric motility could be attributed to meal size
50 and stomach temperature (at least for meals of mackerel), but we did not measure surface
area of prey items or changes in seawater dissolved oxygen concentration, both of which can influence motility (Schurdak and Gruber 1989, Mayer 1995).
The results of the FFT suggest that diel changes in gastric motility exist regardless
of whether the sharks fed. The 23.4 ± 1.8 h periodicity in motility is most likely a result
of the diel fluctuations in stomach temperature, which in turn are related to daily
fluctuations in ambient water temperature. Motility was highest in the afternoon when
water temperatures were also highest and lowest during the early morning hours when water temperatures were also lowest (see stomach temperature data in figure 2). Based on the data from one fasting shark that showed lower pH values during early morning hours (between 0600 and 0800), we hypothesize that blacktip reef sharks preferably
forage during periods (or areas) of lower temperature (in our study such conditions
occurred in early morning, 6 – 8 AM) although they may feed opportunistically at all
times of the day. As such, the period of gastric accommodation (low motility) following
feeding coincides with periods of low temperature, with increased gastric motility
occurring during periods of increased temperature. Our sharks also showed a periodicity
in motility with a frequency of 2.0 ± 0.3 h, which, because this cycle was absent from
sharks that were fasted, may represent regular periods of stomach contractions involved
with mixing stomach contents and passing chyme into the small intestine. In vertebrates,
gut motility during the interdigestive state (fasting) is characterized by migrating motor
complexes (MMC), which consist of periods of quiescence (phase I), periods of irregular
single contractions (phase II), and periods of strong contractions (phase III, Mayer 1995,
Holmgren and Holmberg 2005). The interdigestive state in blacktip reef sharks was not
51 characterized by long periods of quiescence, nor was there an obvious transition between phase II and III, which are similar to the results found for rainbow trout (Olsson et al.,
1999).
In conclusion, gastric digestion in blacktip reef sharks appears to be a function of
abiotic and biotic variables. While foraging behavior (and subsequent optimal foraging
theory) is a function of the tactics used to capture prey, it also may optimize digestive
efficiency or energy extraction from prey items (Hume 2005). By quantifying the effects
of prey type, meal size, and stomach temperature on gastric digestion in sharks under
semi-natural conditions, we can make predictions of foraging strategies in the field.
Subsequent studies will aim to quantify gastric processes in free-ranging sharks in the
field to test these hypotheses.
52 Chapter III
A new acoustic pH transmitter for studying the feeding habits of free-ranging
sharks
ABSTRACT
Little is known about the feeding habits of large free ranging fish, due in large part to lack of an appropriate technique for quantifying feeding variables. A previous study demonstrated that changes in gastric pH can be used as a proxy for feeding events in free-ranging sharks. Here we describe the development of a new acoustic pH transmitter to remotely measure gastric pH in sharks in the field. The transmitter consists of a dual sensor (pH and temperature) continuous pinger, and was tested in captive adult blacktip reef sharks (Carcharhinus melanopterus). The transmitter was retained in the shark’s stomach from between 5 - 12 d. The empty stomach had a low pH (1.6 ± 0.2) and feeding induced a rapid increase in gastric pH, which was clearly distinguishable from baseline levels. Meal size showed a significant linear relationship with the magnitude of the pH changes. Measurement accuracy of the pH transmitter ranged from 0.05 – 0.9, although resolution of the VR100 receiver was 0.1 units. The pH transmitter can be used to determine when free-ranging sharks in the field are feeding and hence quantify feeding chronology, frequency and daily ration.
INTRODUCTION
Sharks are widely thought to play an important role in structuring marine communities but to date most evidence for this comes from analyzing stomach contents
53 of dead sharks and laboratory experiments to determine gastric evacuation rates of
juvenile sharks (Wetherbee et al., 1990, Cortes 1997). Stomach content analyses
traditionally involve sacrificing a large number of animals in order to achieve adequate
sample sizes and this is increasingly undesirable because shark populations in many areas are already in rapid decline due to over-harvesting (Cortes 1997, Baum et al., 2003). Yet in order to better understand the full impact of shark predation on marine ecosystems, and to predict what may happen if shark populations are depleted, we need to know what sharks eat, how often they feed and how much they typically consume (Wetherbee et al.,
1990, Papastamatiou and Lowe 2004).
The stomach is the first site of digestion in vertebrates, and is responsible for the breakdown of prey items into semi-liquid chyme, which it achieves by secreting concentrated hydrochloric acid (HCl) and the inactive enzyme pepsinogen. The HCl breaks down prey hard parts and also converts pepsinogen into pepsin, a proteolytic enzyme (see Papastamatiou and Lowe 2004, 2005). Due to these physiological principals, a specific change in gastric pH should occur after a shark consumes a prey item. Studies with captive sharks using autonomous pH data-loggers have shown that by measuring gastric pH continuously, time of feeding can be determined and meal size estimated (Papastamatiou and Lowe 2004, 2005). However, the pH data-loggers used in the captive studies are not practical for field studies because the data-logger must be retrieved for the data to be collected. Here we describe the development of an acoustic pH transmitter, which will enable gastric pH to be measured remotely in free-ranging sharks, hence allowing the technique to be applied to the field.
54 MATERIALS AND METHODS
Study site
We maintained captive adult blacktip reef sharks (Carcharhinus melanopterus) in
a pen situated in a lagoon at the Hawaii Institute of Marine Biology (HIMB). The water in the pen is tidally flushed and habitat consists of coral rubble, coral ledges, and sand
with a maximum depth of 3 m. Sharks used for experiments were moved through a gate
to an adjacent experimental pen (10 x 20m), where they were acclimated for a week or
until they resumed normal feeding. No more than two sharks were maintained in the
experimental pen at any one time.
pH transmitter
We measured gastric pH using a pH/temperature dual sensor continuous pinger
(Vemco ltd, Nova Scotia). The sensor consists of a glass micro-electrode, and a
reference electrode with a free-diffusion liquid junction connected to a piston-driven
reservoir of electrolyte (Peters 1997a). The piston ensures a continuous supply of fresh
electrolyte is passed over the reference electrode, greatly reducing measurement drift
over a 16 d period, or until the reservoir runs out of fluid (Peters 1997a, b).
The reference electrode was coupled to a transducer, a 3.6 V lithium battery, and
all electronics were encased within a titanium shell (total length of pH transmitter: 17 x
2.5 cm, mass in air = 170 g, nominal battery life 50 d, fig.7). A temperature sensor
encased in the electronics also recorded temperature. Both pH and temperature
recordings were converted by the transducer into an acoustic signal with a carrier
frequency of 60 kHz and a power output of 157 dB. The transmitter was a two channel
55 Figure 7. Acoustic pH transmitter, with transducer and pH electrode labeled.
56
10 cm
Transducer pH electrode
57 continuous pinger, which transmits a continuous sequence of pings with three time
intervals; a fixed interval of 1150 ms, followed by a signal for temperature and pH
respectively. Changes in either temperature or pH result in a change of the interval of the
emitted signal. The sensor measures pH between 0 – 9 units, and temperature between -5
– 35 °C. Due to the large amount of drift associated with measuring pH, the sensor had
to be calibrated before every use. We calibrated the sensor using NBS standard pH
buffers (1.68, 4.01, 6.86) and programmed the calibration parameters into a Vemco
VR100 receiver. We deployed an omni directional hydrophone connected to the VR100
receiver, in the experimental pen. The VR100 recorded and stored all data received and were downloaded daily to a laptop computer.
To insert the pH transmitter, we restrained sharks on a stretcher, inverted them to induce tonic immobility (a trance like state), and anaesthetized them with a 0.15 g/L solution of MS-222. We then inserted a lubricated PVC pipe through the mouth into the stomach and dropped the transmitter through the pipe with the sensor pointing towards the caudal fin. The shark was then revived, released and fed meals of mackerel (Scomber spp.) over the next 5 to 12 d. After each shark regurgitated the pH transmitter, we re- calibrated the electrode in pH buffers, and used the pH drift model described by Peters
(1997b) to estimate the uncorrected errors associated with drift of the electrode during deployment.
In order to quantify the affect of meal size on gastric pH changes, we determined
the area underneath the feeding induced change in the pH-time curve using ArcView
(ver. 3.2). We then used multiple regression analysis to determine the influence of stomach temperature and meal size (expressed both in g and percentage body weight
58 %BW) on the area under the pH-time curve. Temperature and meal size were also set as an interactive term.
RESULTS
We deployed the pH transmitter in three blacktip reef sharks (Total Length (TL) =
148.3 ± 7.6 cm, mass = 23 ± 3.6 kg,) for periods ranging from 5 – 12 d. During deployment, pH error within the range measured in the stomach was between 0.05 - 0.45
(Table 3), although resolution of the VR100 receiver was 0.1 units. However, drift of the
pH electrode was not linear as error ranged up to 0.92 in the pH 6 range, for one shark.
Stomach pH averaged 1.6 ± 0.2 (± 1SD) during periods of fasting but increased
rapidly to a peak of 4.1 ± 0.8 following ingestion of food, and then declined more
gradually back to baseline levels (Fig.8). Multiple regression analysis revealed that meal
size (g) influenced area under the pH-time curve (p = 0.001, t = 3.36), but stomach
temperature had no significant affect (p = 0.881, t = -0.15). When analyzed
independently, meal size had a significant influence on area under the pH time curve
when expressed as % BW (p = 0.01, r2 = 0.52, F = 9.89), and in g (p = 0.001, r2 = 0.70, F
= 20.9, Fig.9).
DISCUSSION
The pH transmitter provides viable data on changes in gastric pH for up to 12 d.
Measurement drift (0.05-0.45) was generally low within the typical range of pH values
observed in the blacktip stomach (pH 1-4). Custom made software can be used to
59 Table 3. Summary information for blacktip reef sharks used in experiments. Error analysis for the pH electrode is also given over three hypothetical pH values using the uncorrected pH drift model described by Peters (1997a).
Shark # Total Mass Sex Deployment pH drift length (kg) duration (d) (cm) 2 4 6 1 155 26 F 7 0.45 0.24 0.92 2 140 19 M 12 0.38 0.27 0.15 3 150 24 F 5 0.14 0.05 0.24
60 Figure 8. Continuous measurements of gastric pH and temperature in three free- swimming blacktip reef sharks (Carcharhinus melanopterus, a-c equivalent to shark # 1-
3). Data was obtained using a dual sensor pH/temperature transmitter. Arrows point to feeding events and meal size is expressed as a percentage body weight. “?” indicates a meal of unknown size.
61 a 10 30 25 8 0.81 0.44 20 6 15 pH 4 10
2 5 (C) Temperature 0 0 0123456 Time (d) b 10 30 ? 25 8 0.8 0.25 0.75 20 6 15 pH 4 10
2 5 (C) Temperature 0 0 24681012 c Time (d) 10 30 0.58 8 25 20 6 15 pH 4 10
2 5 (C) Temperature
0 0 012 Time (d)
62 Figure 9. Linear regression analysis between meal size and area under the pH-time curve from blacktip reef sharks, Carcharhinus melanopterus. Meal size is expressed as % BW
(a) and in g (b). Data was obtained using a pH/temperature transmitter.
63 a 1.6
1.2
y = 0.79x + 0.08, R2 = 0.52 0.8 Area
0.4
0.0 0.0 0.4 0.8 1.2 Meal size (%BW) b 1.6
y = 0.004x - 0.11, R2 = 0.70 1.2
0.8 Area
0.4
0.0 0 50 100 150 200 250 300 350 Meal size (g)
64 interpolate and correct for drift of the electrode if the transmitter is recovered (see Peters
1997a, b), although such recoveries are unlikely in field studies of sharks. The sharks
used in the present study could only dive to a maximum depth of 3 m, and animals diving
to greater depths may cause additional drift of the electrode. The flow rate of electrolyte
out of the capillary junction of the reference electrode is crucial to the performance of the
electrode, and suboptimal flow rates or electrolyte depletion, will increase measurement
drift. Therefore, a larger electrolyte reservoir could increase the length of time over
which the electrode can accurately measure pH. The current pH transmitter has a depth
rating of a 100 m, but embedding all electronic components in epoxy will greatly increase the depth rating (Peters 1997b).
Regardless of measurement accuracy, feeding events were clearly distinguishable from background variation in pH. Feeding resulted in a rapid increase in pH followed by a more gradual decrease to baseline levels. The rapid increase in pH is caused by seawater and the prey items buffering the small amounts of gastric acid present in the
stomach during fasting (Papastamatiou and Lowe 2004). The presence of prey items in
the stomach then causes distention of the mucosa wall and the release of secretagogues,
all of which increase gastric acid secretion and the subsequent re-acidification of stomach
contents (Papastamatiou and Lowe 2004, 2005). As meal size increases, a greater mass
of prey (most of which are weakly acidic or neutral in pH) is in the stomach, and hence
the time taken for gastric contents to re-acidify also increases.
The diet of blacktip reef sharks in the field consists primarily of reef fish
(teleosts), crustaceans, cephalopods, and in some locations reptiles (Lyle and Timms
1987, Cortes 1999). Although daily ration has not been determined for this species, for
65 other carcharhinids it has been estimated at approximately 1-2 % BW/day (Wetherbee et al., 1990), within range of the linear regression calculated in this study, relating meal size
to the postprandial changes in gastric pH. Extrapolating the regression to zero on the y- axis, predicts that the smallest meal size estimated using the transmitter would be 25 g.
However, some caution will be required when applying the technique to the field, as gastric evacuation rates (and presumably post-prandial changes in gastric pH) are thought to be a function of prey lipid content (e.g. Anderson 1999). The prey used in the current experiments (mackerel) most likely have higher lipid contents than prey consumed by blacktip reef sharks in the field, potentially underestimating meal size (high lipid prey will have longer gastric evacuation times).
The changes in gastric pH and the linear response of time taken to re-acidify stomach contents, suggests that feeding chronology, frequency and meal size can be determined in free-ranging sharks. Although other similar sized marine organisms may differ in their gastric physiology, it is likely that the transmitter can also be used with large teleosts, marine mammals and birds. The primary factor limiting application of the technique is the size of the species in question, as is it is unlikely than an animal smaller than 1 m will tolerate the transmitter. However, size of the transmitter can be reduced by selecting for a smaller transducer and battery (also reducing life span of the unit and signal strength).
66 Chapter IV
Scale-dependent effects of habitat on movements and foraging strategies of blacktip
reef sharks, Carcharhinus melanopterus, at Palmyra atoll: a predator dominated
ecosystem
ABSTRACT
The effects of habitat on the ecology, movements, and foraging strategies of marine apex predators are largely unknown. We used acoustic telemetry to quantify the movement patterns of blacktip reef sharks (Carcharhinus melanopterus) at Palmyra Atoll
National Wildlife Refuge. Sharks had relatively small home ranges over a time-scale of days to weeks (0.55 ± 0.24 km2), and showed strong site fidelity to sand-flat ledges within the west lagoon over a 3 year period. Sharks showed evidence of diel and tidal movements, and utilized certain regions of the west lagoon disproportionately. There were ontogenetic shifts in habitat selection, with smaller sharks showing greater selection for sand-flat habitats, and pups (total length 35 – 61 cm) utilizing very shallow waters on flats, potentially as nursery areas. Adult sharks selected ledge habitats and had lower rates of movement when over sand-flats and ledges than they did over lagoon waters.
Fractal analysis of movements showed that over periods of days, sharks used patches that were 3 – 17 % of the scale of their home range. Repeat horizontal movements along ledge habitats consisted of relatively straight movements, which theoretical models consider the most efficient search strategy when forage patches may be spatially and temporally unpredictable. Although sharks moved using a direct walk while in patches, they appeared to move randomly between patches. Micro-habitat quantity and quality has large effects on blacktip reef shark movements and path structure, which has
67 consequences for the life-history characteristics of the species and potentially the spatial
distribution of behaviorally mediated effects on lower trophic levels throughout the
Palmyra ecosystem.
INTRODUCTION
Apex predators are thought to exert top-down control on marine ecosystems
through both density and trait-mediated interactions (Bascompte et al., 2005, Preisser et
al., 2005). While ecological processes require an understanding of the diet of the predator, of equal importance is an understanding of activity patterns, habitat utilization and foraging strategies. Foraging theory predicts that animals will select habitats which provide the greatest return in some form of currency such as prey encounter rate
(Stephens and Krebs 1986). However, for many animals it is difficult to distinguish habitat selection for foraging purposes from those associated with mating or predator
avoidance. Most adult apex predators do not have to invest much energy into anti-
predatory behavior, and since most reproductive behavior is seasonal, it is possible to
separate foraging from these other behaviors. Ontogenetic shifts in habitat selection can
further contribute to an understanding of why animals select the habitats they use.
Many species of shark are considered apex predators but there may be large
geographic differences in their ecological significance (e.g. Stevens et al., 2000,
Bascompte et al., 2005). The current worldwide decline in many shark populations
further highlights the importance of understanding their ecological significance in
multiple habitats and locations (e.g. Stevens et al., 2000). While shark movements and
habitat utilization have been studied in several locations (e.g. McKibben and Nelson
68 1986, Rechisky and Wetherbee 2003) there are precious few studies that have quantified
fine spatial scale habitat selection in relation to foraging strategies (Morrissey and Gruber
1993b, Heithaus et al., 2002, 2006). There are presently no studies that have quantified both high spatial and temporal scale movement patterns for any shark species.
The blacktip reef shark (Carcharhinus melanopterus, Quoy and Gaimard 1824) is a common shark species found on coral reefs of the Indo-Pacific (Compagno et al., 2005) and qualitative observations suggest that they occupy shallow reefs and sand-flats at both atolls and high islands (Hobson 1963, Stevens 1984). Blacktip reef sharks are one of the most abundant apex predators at many atolls (Stevens 1984, Compagno et al., 2005), although human impacts have reduced their numbers at many locations (e.g. Sandin et al.,
2008). Dietary analysis suggests that C. melanopterus are tertiary predators and feed primarily on teleosts, crustaceans, cephalopods, and in some areas reptiles (Cortes 1999).
These factors raise the possibility that blacktip reef sharks could exert top-down control on many coral reef ecosystems. However, currently no detailed analysis of blacktip reef shark movement patterns, space utilization and habitat selection exists.
Palmyra Atoll is part of the Line Island chain in the central Pacific Ocean, and has been a U.S. National Wildlife Refuge since 2001, essentially making the atoll a no-take marine reserve. As a consequence of reduced human impacts, a healthy population of fish apex predators exists at the atoll, making up over 65 % of the total fish biomass
(Sandin et al. 2008, DeMartini et al., 2008). Diver observations show that blacktip reef sharks are the most abundant predator in the inner lagoons and sand-flats at Palmyra
(Hobson 1963, Friedlander et al. 2007, DeMartini et al., 2008). However, the ecological impacts of blacktip reef sharks are partially a function of which habitats the sharks select
69 for, and how they behave in such habitats. Knowledge of habitat selection is particularly crucial for understanding predator-dominated coral reef ecosystems, where the behavioral response of prey to predators appears to largely dictate resulting ecosystem trophic structure (Knowlton and Jackson 2008).
We used acoustic telemetry to quantify the movement patterns, habitat utilization and foraging strategies of blacktip reef sharks at Palmyra. Our specific objectives were:
1) determine the degree of site fidelity shown by the sharks to sand-flats within a lagoon at Palmyra Atoll over different temporal and spatial scales 2) determine if there are any diel or tidal changes in movement patterns, 3) quantify the selection or avoidance for microhabitats in the lagoon over the scale of hours to days, and 4) use fractal analysis to quantify the movement path structure and subsequent foraging strategies of blacktip reef sharks at Palmyra over short time periods (hours to days).
MATERIALS AND METHODS
Study Site
Palmyra Atoll (N 5°53’, W 162°05’) is part of the Line Island chain located just north of the equator (Fig.10). Two primary lagoons (west and east) are linked by a small channel, while a larger channel links the west lagoon to the outer reefs (Fig.10). The lagoons have a maximum depth of 50 m, with a mud/sand substratum causing low water visibility, while the outer fore-reefs are characterized by steep slopes with high coral cover and high visibility. Due to Palmyra’s location in the Inter-tropical Convergence
Zone, the atoll receives up to 500 cm of rainfall per year, and consequently terrestrial habitat is largely rain-forest (Fig. 10). In addition to Palmyra’s refuge status, only a small
70 Figure 10. a) Map of the Line Islands relative to the central Pacific Ocean. b) Islands of the Line Islands including Palmyra atoll. c) VR2 positions and detection radii within
Palmyra Atoll. VR2’s in the west lagoon are Banjos (B), Eddies (E), Nursery (N),
Airport (A), and Midchannel (M), and in the east lagoon, Sixes (S), Cookies (C), and
Downeast (D). The location of Banjos (circle), Nursery (triangle) and the main Channel
(Ch) sand-flats are also shown. Stars in c) show known locations of blacktip reef shark pups.
71
72 crew of up to 17 refuge staff and scientists inhabit the atoll, hence human influences are
maintained at a low level.
Active tracking
Blacktip reef sharks were attracted to the Banjos, Nursery or Channel sand-flats
using squid bait (Fig.10). We then concealed a V13 acoustic transmitter (dimensions 13
x 30 mm, carrier frequencies 62 – 78 kHz, Vemco ltd., Nova Scotia) in a piece of squid,
and allowed one of the sharks to voluntarily consume the transmitter containing bait. To
facilitate longer tracks, we also caught some individuals and surgically implanted the
transmitters into the body cavity. In those cases, we caught sharks on barbless hooks and
brought them alongside the boat where they were restrained, inverted, and placed in tonic
immobility; a trance like state. A small incision was then made through the shark’s
abdominal wall and a transmitter was implanted into the body cavity. The wound was
closed with a single suture and the shark was released. We waited a minimum of 48 h
before initiating tracks of sharks that were surgically fitted with transmitters, to remove
the influence of surgery on movements. A PVC pipe of known length placed on the
sand-flat enabled us to estimate shark total length in instances where the shark was fed a transmitter. For sharks fed transmitters, we discarded the first 2 h of data so as to remove
the influence of feeding and odors on movement patterns. Continuous tracking was
conducted using a kayak technique (Meyer and Holland 2001) and an RJE PRS 275 (RJE
International ltd., Irvine, California) handheld underwater receiver, which enabled us to
track the sharks in very shallow water. All sharks were tracked continuously during
daytime hours with GPS positions taken every 15 min. During tracking we maintained a
minimum 10 - 30 m distance from the shark, but when GPS positions were taken we
73 would move to the location where the shark had previously been, so that we could quantify habitat use. If the shark was over a sand-flat, we were able to determine the exact location of the shark because the shallow water over the flats enabled us to visually track the shark and determine which habitat it was occupying. If the shark was over a
ledge or in deeper lagoon waters, we would get an accurate fix by positioning the kayak
until the acoustic signal strength was the same in all directions, indicating that we were
directly above the animal (ground zero). Notes were taken while tracking to indicate
which habitat sharks occupied when positional fixes were obtained, to ground-truth the
GPS data. Due to safety regulations, continuous tracking could not be conducted at
night. During the night we either; obtained single location checks every two hours, or we
tracked continuously for one hour, every two hours. We estimated positional accuracy of shark locations to be ± 7 - 8 m (based on GPS accuracy).
Nursery delineation
Neonate and young-of-the-year (YOY) blacktip reef sharks were only observed in
very shallow sand-flat habitats, close to shore. As it relates to ontogenetic shifts in
habitat use, and because blacktip shark pups were too small to carry acoustic transmitters,
we sampled these sharks at locations where they were aggregating. Sharks were caught
using a 30 m long seine net that was positioned perpendicular to the shoreline. We then
herded the sharks into the net where they were measured, sexed, weighed, and released.
Data Analyses
Home range
74 All spatial analyses of movement data were conducted in ArcView GIS (ver. 3.2) layered over geo-referenced IKONOS images of Palmyra Atoll. We calculated two metrics as a proxy for shark home range. The Kernel Utilization Distribution (KUD,
Worton 1989) is a probability distribution that represents the area where there is a 95 % and 50 % chance of finding the individual tracked. The 95 % KUD is considered a measure of the overall home range of the animal, while the 50 % KUD is more representative of the area of core use (e.g. Heupel et al., 2004). A Minimum Convex
Polygon (MCP) is the area of a polygon formed by connecting the outer position fixes of an animal’s movements. Both estimates were calculated using the Animal Movements extension with ArcView GIS ver. 3.2 (Hooge and Eichenlaub 1997). Home range was only calculated for sharks that were tracked for a minimum of 24 h continuously, so as to include at least one complete diel cycle. Areas of the KUD and MCP which extended onto land were manually removed. Multiple regression analysis was used to determine the influence of shark total length (TL) and water temperature on 95 % KUD area. As a quantitative measure of the shape of the sharks home range, we determined the Index of
Eccentricity (ECC), ECC = l / w, where l = maximum length of the animals activity space and w = maximum width of the activity space. A circular activity space will produce
ECC = 1, while ECC values greater than 1 indicate an asymmetrical shaped activity space
(Morrissey and Gruber 1993a, Rechisky and Wetherbee 2003).
We calculated two measurements of site fidelity for blacktip reef sharks. The
Linearity Index Li = (Fn – F1) / D, where Fn – F1 is the distance between the first and the last location fixes of the shark, and D is the total distance traveled by the animal. An animal moving in a nomadic fashion should have Li approach 1, while an animal
75 exhibiting strong site fidelity should have Li approach 0. We also calculated the Index of
Reuse (IOR), IOR = [OV(A1+A2)] / (A1+A2), where [OV(A1+A2)] is the area of
overlap between two daily activity spaces (e.g. 12 h) and (A1+A2) is the total area of
both daily activity spaces. IOR = 1 indicates 100 % re-use of an area on a daily basis
(site fidelity) while an IOR = 0 indicates 0 % overlap in area (nomadic behavior). In
order to determine if blacktips showed daily shifts in core areas, we also calculated IOR
values between daily 50 % KUD areas.
To test for diel behavior, we compared daytime and nighttime activity space size
(using the MCP measurement) for each shark using a Student’s t-test. We also
determined Rate of Movement (ROM) for each shark during day and night periods, with
ROM defined as the distance moved by the shark between two points, divided by the time
taken to swim between the points. To evaluate the effects of diel and tidal periods on
shark ROM, we categorized all ROM values into a) day vs. night and b) tidal period (high
slack, low slack, flood, ebb). We tested diel and tidal effects simultaneously by dividing
ROM data into 8 diel and tidal groupings and using a one-way ANOVA with a Tukey-
Kramer aposteriori test. ROM data were converted to Body Lengths / min (BL min-1) to control for shark total length. Data were square root transformed to meet the assumptions of normality (Shapiro – Wilk W test). ROM data represents speed over ground, hence an animal swimming in a straight line will have higher ROM than an animal foraging over a small area (see Phillips et al., 2004).
Habitat utilization
The inner lagoons at Palmyra consist of four microhabitat types: sand-flats, reef ledges, deeper sand-flats, and lagoons. Sand-flats are extensive areas with water depth <
76 2 m and benthic habitat is sand/coral rubble, while deeper sand-flats are areas in the
central lagoons where water depth is 2 – 5 m. Lagoons comprised all other regions
within the atoll where water depth exceeds 5 m and benthic substratum is primarily fine
sand/mud. Ledges are located at the boundary between sand-flats and lagoons. Based on
results of fractal analysis (see below) we considered ledge habitats to include the area
within 20 m of either side of the drop off. For each shark, we determined the number of
position fixes that occurred in each of these habitat types. We then calculated the area of
each of these habitats in the west lagoon, as a percentage of the total area, using the geo-
referenced IKONOS image of Palmyra. We used a Chi-squared test to determine if
habitats utilized by the sharks differed significantly from expected based on overall
habitat available. We then used the modified Strauss linear index of food selection, L = ri
- pi, where L is the habitat selection value, ri is the percentage use of habitat i, and pi is the percent availability of habitat i (Morrissey and Gruber 1993b). Values of L > 0 suggest statistical selection for a particular habitat while values of L < 0 suggest avoidance of a habitat. We determined habitat selection values for all sharks combined, but then performed least-squared linear regression analysis between individual shark length and
habitat selection values for sand-flat, ledge, and lagoon habitats. In order to evaluate the
effect of habitat type on speed over ground, we also quantified ROM for each shark while
they were moving over ledge, sand-flat and lagoon habitats and utilized an ANCOVA test
using ROM as the dependent variable, habitat as an independent variable and shark total length as a covariate. A Tukey’s test was then used to determine the location of pair-wise
differences. For habitat analysis ROM data were log transformed to meet the
assumptions of normality.
77 Fractal analysis
A fractal dimension is a measure of tortuousity of a movement path, and can
range from 1 for a straight line to 2 for a path so tortuous that it completely covers a
plane (Nams 1996, 2005). Fractal measures of animal movements are generally scale-
dependent as the tortuousity of a path will vary based on the scale at which it is viewed.
Therefore, by examining how the fractal value (D) varies with scale for a movement path,
we can quantify the scales at which the animal views its environment and also detect
patch use (Nams 2005). For a more detailed description of the use of fractal analysis in
animal ecology see Nams (1996, 2005), and Doerr and Doerr (2004). However, a caveat of using fractal analysis to describe animal movements is that if the animal is moving
using a correlated random walk (CRW), then the changes in Fractal D with scale may not
be caused by a change in tortuousity with scale (Nams 1996, 2005). A CRW occurs
when each step of an animal’s movements relate to the following rule: θi = θi-1 + έi, where
θi is the direction of step i and έi is a random angle drawn from a normal distribution.
Therefore, turning angles are independent of previous turning angles. To determine if
sharks were moving using a CRW, we calculated the CRWDiff test described by Nams
k 2 2 1 n − RER n )( k 2 (2006a), CRWDiff= ∑ 22 − REln 2 )( where Rn represents the observed mean n =1 n
(net distance)2 for each number of n consecutive moves, E is the expected mean (net distance)2 according to the CRW equation described by Kareiva and Shigesada (1983), l
is the mean step length, and k is the turning angle concentration (Nams 2006a). CRWDiff is therefore a measure of how net displacement of an animal’s movements varies from that predicted by a CRW. If CRWDiff > 0 then the animal shows greater net displacement
78 than a random walk, while if CRWDiff < 0 the animals movements are more constrained
than a CRW.
Two fractal measures were used to analyze the movement patterns of blacktip reef
sharks. Fractal Mean was used to estimate an overall fractal D value for each blacktip
reef shark, by using the traditional divider method (Doerr and Doerr 2004). A range of
dividers are used to determine the length of a movement path, with path length
decreasing as divider size increases (Mandelbrot 1967, Doerr and Doerr 2004). A log-log
plot of divider size versus path length is then generated, which yields a line with slope 1-
D, and can be described by L(G) = kG1-D, where L(G) is path length, k is a constant and
G is divider size. If the movement path is more tortuous and has a greater number of
turns, then the slope of the line will also increase, and the path has a higher fractal D.
Fractal D also incorporates replication by measuring path length twice for each divider
size, by running the dividers both forward and backward along the path, which reduces
bias associated with previous Fractal D measures (Nams 2006). To measure the change
in fractal D with scale, we used the VFractal estimator described by Nams (1996).
VFractal calculates the fractal values based on the turning angle between consecutive
locations, and its associated error estimator (Nams 1996). We used the VFractal
estimator in Fractal ver. 5.0, for divider sizes ranging from 10 – 1000 m. The 95 %
confidence intervals were calculated using a bootstrapping procedure, which randomly
selects turning angles from the movement path to calculate VFractal, with 1000 replicates
(Nams 1996).
To detect patch use, we determined the correlation in tortuousity between adjacent
path segments for a range of divider sizes. If the divider size is below the size of a patch
79 used by the animal, then it is likely that consecutive path segments will be either inside or
outside the patch hence the tortuousity correlation between adjacent path segments
should be positive. As divider size approximates patch size then it’s likely that one path
segment will be inside the patch (with high tortuousity) while the adjacent segment is
outside the patch (with low tortuousity), hence the correlation should be negative.
Therefore, a positive correlation followed by a negative correlation is indicative of patch
use and size (Nams 2005). If there is no patch use, then there should be no correlation
between patch segments and the correlation should be zero regardless of whether the
animal is moving in a random or directed manner (Nams 2005). All fractal measures and
correlation statistics were calculated in Fractal ver. 5.0.
Long term movements
To quantify longer term site fidelity of blacktip reef sharks to different reef flats,
we established an array of 8 omni-directional automated underwater acoustic receivers
(model VR2, Vemco, Nova Scotia) throughout the west (five receiver’s) and east lagoon
(three receiver’s, Fig.10). The receivers were moored to the mud/silt lagoon substratum
in depths of 10 – 30 m, with the receivers suspended 10 - 15 m below the surface. We
surgically implanted 8 blacktip reef sharks within the west lagoon with Vemco V8SC-2L transmitters (8mm diameter x 20 mm length). Each transmitter produces a unique pulse code that can be detected by the VR2 receivers when a tagged shark is within range (300 m) of the receiver. These transmitters had a nominal battery life of one year. VR2 receivers were retrieved, downloaded and redeployed every 4 - 6 months.
We determined the number of detections at each VR2 for each shark as a percentage of the total number of detections. To determine if sharks were
80 disproportionately using certain areas more than others, we compared proportion of
detections between the different VR2 receivers. The data did not conform to the
assumptions of parametric statistics despite transformation and a non-parametric Kruskal-
Wallis Rank Sum Test was utilized. To examine temporal patterns of movement, we
used a Fast Fourier Transformation (FFT). An FFT decomposes time series data into
component frequencies, and then searches the data-set for cyclical patterns. Sinusoidal
patterns with dominant frequencies can be identified as peaks in a power spectrum. As
such, FFT analysis can identify diel, tidal or seasonal patterns in animal movements (e.g.
Meyer et al., 2007). We binned the number of VR2 detections in every hour for each day
of the VR2 deployments and smoothed the data using a Hamming window before
applying the FFT. A Hamming window reduces the effects of adjacent spectral
components, which can potentially generate biologically meaningless frequency peaks.
RESULTS
Active tracking
Home range
We actively tracked 14 blacktip reef sharks (Total Length (TL) 100 ± 17 cm,
mean ± 1SD) for periods ranging from 4 – 72 h, between February 2005 and September
2007 (Table 4). Although this represents continuous tracking times, we would also periodically re-locate sharks up to 14 days following the start of the track. Ten sharks were fed transmitters, while four animals had transmitters surgically implanted. There
81 Table 4. Blacktip reef sharks (Carcharhinus melanopterus) actively tracked at Palmyra atoll. TL, Total length. Tagging location indicates reef flat where sharks were tagged.
Sharks were either fed transmitters or had them surgically implanted.
Shark TL Sex Hours Days Month Tagging Tagging # (cm) tracked tracked location method 1 80 - 24 1 Feb 05 Banjos Fed 2 70 - 51 4 July 05 Banjos Fed 3 110 - 9 1 July 05 Banjos Fed 4 110 - 48 4 Mar 06 Banjos Fed 5 95 - 8 1 Mar 06 Banjos Fed 6 100 - 7 1 Mar 06 Nursery Fed 7 65 - 6 2 Mar 06 Nursery Fed 8 120 - 10 1 Nov 06 Banjos Fed 9 107 M 72 10 Nov 06 Banjos Implant 10 108 M 4 1 Nov 06 Nursery Implant 11 110 - 17 2 Nov 06 Banjos Fed 12 80 - 24 3 Nov 06 Banjos Fed 13 125 F 50 4 May 07 Channel Implant 14 110 F 10 7 Sept 07 Channel Implant
82 was no significant difference in overall ROM between sharks that were fed transmitters
(11.3 ± 8.7 m/min) and those that had them surgically implanted (11.4 ± 9.4 m/min, t =
1.97, p = 0.77). Sharks moved over a limited area, with repeated use of core locations on
a daily to weekly basis (Fig.11). Home range estimates were small, with average 95 %
KUD areas of 0.55 ± 0.24 km2, and MCP areas of 0.33 ± 0.26 km2, while the maximum
linear dimension of the home range was 1.4 ± 0.3 km (Table 5). The 95 % KUD
estimates were larger than MCP estimates for five of the six sharks examined, although
the difference was not significant (t-test paired for means, t = 1.45, p = 0.76, Table 5).
There was no effect of shark TL, water temperature, or the interaction term on 95 %
KUD area (F = 0.11, p = 0.77). Blacktip reef sharks tended to have activity spaces which
were asymmetrical and oblong in shape, as indicated by the high ECC values (4.8 ± 2.3,
Table 5) and there was no influence of shark TL on ECC (F = 1.1, d.f. = 13, p = 0.30).
The repeated use of core areas by blacktip reef sharks was also apparent based on
the low Li values (0.121 ± 0.096, Table 4). The highest Li value (0.280) was for the shortest tracks (9 and 4 h), but much lower values were obtained from sharks tracked for longer periods. For example shark # 9 was tracked for 72 h and had Li = 0.007, and shark
# 4 tracked for 48 h had Li = 0.028 (Table 5). There was no influence of shark TL on Li
(F = 0.002, d.f. = 13, p = 0.89). IOR values (0.19 ± 0.11) were lower than expected based on the low Li values (Table 5). However, the lower IOR values in general were
due to low overlap in consecutive 50 % KUD activity spaces. The IOR between 50 %
KUD for day 1 and 2 in blacktip # 9 was 0.005, for blacktip # 4 was 0.189, blacktip # 2
was 0.024, and blacktip # 13 was 0.0. When sharks were re-located on subsequent days,
they were located within or near the original 50 % KUD, which resulted in low Li values.
83 Figure 11. Home range of six blacktip reef sharks at Palmyra atoll. Polygons are 95 %
KUD’s, and dots are shark locations. The size and sex (where known) of each shark is given in the figure.
84
85 Table 5. Home range and movement statistics for blacktip reef sharks (Carcharhinus melanopterus) actively tracked at Palmyra atoll. TL Total length, KUD 95 % Kernel
Utilization Distribution, MCP Minimum Convex Polygon, Max dim. maximum dimension of home range, ECC Index of Eccentricity, IOR Index of Re-use, D Fractal value. Means and standard deviation (SD) are also given. Sharks with no values in cells, had insufficient data to detect patch size. Patch sizes as a percentage of home range length are also given.
Shark TL 95% MCP Max dim Linearity ECC IOR D Patch size (m) Patch size/ # (cm) KUD (km2) (km) index home range (km2) length (%) 1 85 0.30 0.22 1.0 0.085 4.1 0.20 1.3679 26-30, 76-88 3 - 9 2 75 0.20 0.19 1.2 0.038 5.2 0.23 1.1813 32-76 6 3 110 - - 1.5 0.280 5.7 - 1.1615 56-96 4 110 0.81 0.91 1.2 0.028 1.1 0.34 1.2247 168-200 17 5 95 - - 1.4 0.238 8.1 - 1.1550 - 6 100 - - 0.5 0.214 1.1 - 1.1929 - 7 70 - - 0.5 0.086 1.7 - 1.2506 - 8 120 - - 1.4 0.050 4.5 - 1.2270 75-96 9 107 0.31 0.25 1.3 0.007 4.9 0.20 1.2215 23-44, 115-200 3 - 15 10 108 - - 1.7 0.280 6.4 - 1.3994 - 11 110 - - 0.8 0.105 5.4 - 1.3256 - 12 80 0.60 0.25 1.4 0.167 5.5 0.17 - - 13 125 0.67 0.22 1.7 0.031 8.8 0.01 1.2910 47-65 4 14 110 0.94 0.29 1.8 0.085 5.2 - - - Mean 99 0.55 0.33 1.2 0.121 4.8 0.19 1.2499 SD 19 0.28 0.26 0.4 0.096 2.3 0.11 0.0800
86 Blacktip reef sharks did not exhibit any detectable diel shifts in activity space size
or location (day 0.17 ± 0.15 km2, night 0.14 ± 0.16 km2, paired t-test for mean, t = 0.42,
d.f. = 12, p = 0.68,). However, there were significant differences in ROM values when
data was separated by diel tidal periods (ANOVA, F = 2.63, d.f. = 257, p = 0.012).
Sharks swam with a greater speed over ground during ebb tides at night (18.1 ± 8.2
BLmin-1) compared with flood tides at night (8.5 ± 9.5 BLmin-1). Flood and ebb
nighttime ROM values did not differ from any of the other categories. However, it
should be noted that because not all sharks were continuously tracked during nighttime periods, the smallest ROM samples sizes were for nocturnal flood and ebb tides.
Habitat utilization
The observed use of habitats by sharks differed significantly from expected based
on available area of each habitat type (X2 = 16.1, p = 0.01). When data from all sharks were combined, high L values (selection) were obtained for ledge habitats (L = 0.59), while lower L values (avoidance) were obtained for sand-flat (L = -0.14) and lagoon habitats (L = -0.38, Fig.12). Sharks showed neither avoidance nor selection for deeper sand-flat habitats. As sharks increased in size, their selection for sand-flat habitats decreased (L decreased, F = 5.52, d.f = 13, p = 0.04, r2 = 0.36, L = -0.008TL + 0.75,
Fig.12). There was no significant relationship between L for ledge or lagoon habitats and
shark TL (F = 0.062, p = 0.81).
Both shark TL (ANCOVA, F = 7.38, d.f. = 15, p = 0.017) and habitat type (F =
11.83, d.f. = 15, p = 0.001) influenced ROM, although there was no significant
interaction effect on ROM (F = 0.82, p = 0.46). As sharks increased in size, ROM also
87 Figure 12. Habitat selection by blacktip reef sharks at Palmyra atoll. a) Habitat selection for all sharks combined (n = 14). L > 0 suggests selection for a habitat, while L < 0 suggests avoidance of a habitat. Observed use of habitat differed significantly from expected (p = 0.01). b) Relationship between shark total length and selection coefficient for sand-flat habitats (y = -0.008x + 0.75, r2 = 0.34, p = 0.04).
88 a 0.8
0.6
0.4
0.2 L 0.0
-0.2
-0.4
-0.6 sand flats lagoon ledge deep flat
b 0.4
0.2
0.0
-0.2 L (sandflats)
-0.4
-0.6 60 70 80 90 100 110 120 130 Total length (cm)
89 increased, and sharks swam with the greatest speed over ground when over lagoon
habitats, and the lowest when over sand-flats. Sharks moved slower over sand-flat
habitats (7.6 ± 1.4 m/min) than they did over lagoon (16.8 ± 6.6 m/min, p = 0.0007) and
ledge (11.7 ± 2.3 m/min, p = 0.022) habitats. Sharks had higher ROM when over lagoon
rather than ledge habitats (p = 0.049).
Fractal analysis
Blacktips showed more constrained movements than predicted by the Correlated
Random Walk (CRW) model (CRWdiff = -0.128, d.f. = 11, p = 0). Therefore, fractal
analysis was an appropriate technique for analyzing shark movements. The relatively
high D values (1.25 ± 0.08) indicate that sharks had tortuous movement patterns,
characterized by repeated back and forth movements along the reef ledges (Fig.11, Table
5).
Fractal analysis suggests that sharks view their environment at a minimum of two
different scales (Fig.13). When data from all sharks was combined, discontinuities in D
existed at 15 – 67, and > 107 m (Fig.13). At scales between 15 – 67 m movements
appeared to be scale invariant, as there were no changes in D with scale. D started to
slowly increase at scales > 67m and increased more rapidly at scales > 107 m. At scales
> 400 m the confidence intervals were too wide for any conclusions to be made with
regards to movement structure. However, fractal analysis of individual animals indicates that there is some intra-specific variability in behavior (Fig.14). Both changes in
VFractal and correlation coefficients show that shark # 2 used patches at scales of 32 - 76
m (Fig. 14a, b). The shark swam in relatively straight paths up to scales of 30 m, after
which movements became more tortuous, especially at scales > 150 m. Shark # 9 swam
90 Figure 13. Changes in VFractal (D) with scale for movements of all blacktip reef sharks
combined (n=13). Solid line is mean, while dashed lines are upper and lower 95 % confidence intervals. The x-axis is on a log scale. The hatched box shows the location of
a two discontinuities in D. Numbers are x-axis values at location of the discontinuities.
91 2.0
1.8
1.6
107
Fractal D 1.4 31 67 1.2
1.0 10 100
Scale (m)
92 Figure 14. Fractal analysis of blacktip reef shark movement patterns at Palmyra atoll. a) and b) are for a 70 cm TL (shark # 2)individual, while c) and d) are for a 110 cm female shark (Shark # 9). Upper panel shows change in VFractal with scale, while lower panel shows change in correlation in fractal values between adjacent steps. Solid line shows mean values while dotted lines show upper and lower 95 % confidence intervals. Striped rectangular bars show areas of discontinuity in VFractal (upper panel) or scales of patch use (lower panel). Scale values at these locations are given on the figure. The x-axis is on a log scale.
93 a c 2.0 2.0
1.8 1.8
1.6 1.6 200
1.4 1.4 Fractal D Fractal Fractal D Fractal 62 1.2 1.2 33 30
1.0 1.0 10 100 10 100 Scale (m) Scale (m) b d 1.5 1.0
1.0 32 0.5 23 0.5 115 76 0.0 0.0 44
-0.5 Correlation Correlation 200 -0.5 -1.0
-1.5 -1.0 10 100 10 100 Scale (m) Scale (m)
94 in a fairly direct manner up until a scale of 30 m after which a discontinuity and increase in D occurred, with progressively more tortuous movement paths at scales from 30 – 400 m (Fig. 14c). Correlation coefficients show patch use at scales of 30 - 40 m and 115 -
200 m (Fig. 14d). In general, blacktip reef sharks in the west lagoon use patches at scales of 30 - 40 m, 60 – 90 m, and 115 – 200 m, which approximate 3 – 17 % of the scale of their home range (Table 5).
Nursery delineation
In over 500 h of tracking and fishing for sharks on sand-flats, ledges, and lagoons, neonate and YOY sharks were only seen and captured on sand-flats very close to the shore-line (< 1 m), often in water no more than 10 cm deep. In these areas we caught 43 neonate and YOY blacktip reef sharks (TL 46 ± 5 cm, range 34 – 61 cm, 25 females, 18 males, Fig. 10). These potential nursery areas were always located interior from the reef ledge, although it is unknown where the sharks went during extreme low tides (when sand-flats are exposed). Nursery locations only represent areas where we sampled. YOY were observed in areas where we did not sample, and they are always found in the same habitat type (very shallow water over sand-flats, close to the shoreline).
Long term movements
We deployed long term transmitters in 9 sharks (114 ± 10 cm TL, 5 males and 4 females, Table 6) between February 2004 and February 2005. Between February 2004 and October 2007, all 8 (100 %) of our receivers detected 7 of the 9 tagged sharks (78 %) for periods of 444 – 1160 d (Median 926 d, Table 6). There were significant differences in the percentage detections by each VR2 (Kruskal-Wallis, H = 19.84, d.f. = 7, p =
95 Table 6. Summary of acoustic monitoring data for 9 blacktip reef sharks (Carcharhinus melanopterus) tagged in the west lagoon of Palmyra Atoll with long-life acoustic
transmitters. All sharks were tagged with V8 transmitters except for 29* which was
tagged with a V16 transmitter.
Capture Transmitter TL Sex Date Date first Date last Overall Total location code (cm) deployed detected detected detection detections period (d) Banjos 62 122 F 20 Mar 04 24 Mar 04 25 Mar 07 1097 1211 Banjos 56 105 M 20 Mar 04 23 Mar 04 24 Jul 05 489 1240 Nursery 51 111 F 22 Mar 04 23 Mar 04 9 Jun 05 444 527
Nursery 57 112 M 22 Mar 04 25 Mar 04 6 Oct 06 926 4307 Nursery 58 132 F 22 Mar 04 - - - - Nursery 63 101 M 22 Mar 04 22 Mar 04 25 May 07 1160 5057 Nursery 60 108 M 22 Mar 04 31 Mar 04 14 Jan 06 655 141 Banjos 5 114 M 26 Feb 05 - - - - Nursery 29* 123 F 24 Feb 05 25 Feb 05 12 Oct 07 960 150348
96 0.006), with a greater proportion of detections of tagged sharks at the Banjos (median
33.5 %) and Airport (median 15.2 %) receivers than any of the other locations (Table 4).
Sharks showed site fidelity to a small area as 81 ± 12 % of detections occurred at one core receiver for each shark (Table 7). Detected movements were mostly confined to the west lagoon, with only 0 - 4.3 % of detections occurring in the east lagoon (median 0.1
%). Distinct seasonal changes in movements were only apparent in two individuals (29
%), which showed movements to the east lagoon (Fig. 15). Both sharks (one male, one female), made annual movements to the east lagoon starting in late December, over a
three year period. The excursions were brief and occurred periodically over a two month
period, with both sharks returned to the west lagoon daily after excursions.
Spectral analysis showed evidence of diel and tidal effects (Fig.16). Five of the
six (83 %) sharks showed 24 h peaks in the time frequency spectrum, and four (67 %)
showed 12, 6 or 8 h peaks associated with tidal movements (Table 7). However, the
spectral density of the peaks was low, indicating that diel or tidal behavior did not occur
daily, and that there were periods of no detections.
DISCUSSION
Home range size and site fidelity
Blacktip reef sharks at Palmyra Atoll appear to have relatively small home ranges
over the scale of days to weeks. Coastal adult sharks in both tropical and temperate
waters have significantly larger home ranges than the blacktips in the present study
(McKibben and Nelson 1986, Holland et al., 1993, Rechisky and Wetherbee 2003).
Blacktip reef sharks tracked at Aldabra Atoll, Indian Ocean, also showed limited
97 Table 7. Percentage of detections by VR2 receivers at 8 locations throughout the Palmyra
lagoons, for 6 acoustically tagged sharks, and the period of dominant peaks from FFT
analysis. Sixes, Cookies, and Downeast are all located in the east lagoon. Median and
upper quartile (Q3) are also given.
Shark Nursery Banjos Airport Eddies Midchannel Sixes Cookies Downeast Dominant peaks (h) 62 1.2 6.9 88.6 0 2.5 0 0.5 0 24, 12, 8 56 1.5 84.2 13.3 0.7 0 0 0 0 24, 6 51 0.8 0 94.7 0 4.2 0 0 0 24, 8 57 0 80.7 6.1 12.6 0.4 0.1 0 0.1 - 63 4.0 60.1 9.1 18.8 3.9 3.4 0.2 0.4 24 60 0 0 17.0 0 78.0 4.3 0 0 24, 6 Median 1.0 33.5 15.2 0.4 3.2 0.1 0 0 Q3 2.1 81.5 90.1 14.1 22.6 3.6 0.3 0.2
98 Figure 15. Seasonal movements of two blacktip reef sharks from the west to the east lagoon. For clarity, only the detections in the east lagoon have been shown. a) is a 101 cm male, b) a 129 cm F
99 a 00:00:00
20:00:00
16:00:00
12:00:00 Time
08:00:00
04:00:00
00:00:00 4/1/04 10/1/04 4/1/05 10/1/05 4/1/06 10/1/06 4/1/07 Downeast Date Sixes b Cookies 00:00:00
20:00:00
16:00:00
12:00:00 Time
08:00:00
04:00:00
00:00:00 3/1/05 7/1/05 11/1/05 3/1/06 7/1/06 11/1/06 3/1/07 7/1/07 Date
100 Figure 16. Examples of long term movements of two acoustically tagged blacktip reef
sharks. a) scatter plot showing movements for shark 62 and b) associated spectral
analysis (FFT). The periods with dominant peaks in the FFT have been labeled. c)
scatter plot for shark 56 and d) associated spectral analysis. Note the use of different scales on the y-axis for b) and d).
101 a b 00:00:00 10 24 20:00:00 8
16:00:00 6 12:00:00 Time 4 08:00:00
04:00:00 density Spectral 2 8
00:00:00 0 4/1/04 10/1/04 4/1/05 10/1/05 4/1/06 10/1/06 10 20 30 40 Date Period (h)
c d 00:00:00 20
20:00:00 15 24 16:00:00
12:00:00 10 Time 08:00:00 5 6.2 12 04:00:00 density Spectral
00:00:00 0 4/1/04 8/1/04 12/1/04 4/1/05 10 20 30 40 Date Period (h)
Mid channel Cookies Banjos Airport Nursery
102 movements, but moved up to 2.5 km in 7 h (Stevens 1984). Aldabra Atoll (34 km) is a
much larger atoll than Palmyra (12 km) and sharks were tracked by attaching mono-
filament and surface floats to the dorsal fins, which could have affected their behavior
(Stevens 1984). We found no effect of shark size and water temperature on home range
size in blacktip reef sharks. Theory predicts that as an animal increases in size, energetic
requirements and consequently area over which resources are obtained (home range) also
increase (see review in Lowe and Bray 2006). While a number of studies have shown an
ontogenetic expansion in home range with shark length (e.g. Heupel et al., 2004, Garla et
al., 2006), only juvenile lemon sharks (Negaprion brevirostris) in the Bahamas have been
shown to display a linear increase in home range size with shark length over the smaller
size ranges (47 – 100 cm PCL, Morrissey and Gruber 1993a)
Blacktip reef sharks showed a high degree of site fidelity to the west lagoon, in
particular, to core areas within the lagoon for periods over several years. While the
active tracking indicated strong fidelity to the Banjos and Channel sand-flat ledges,
acoustic monitoring data also showed that blacktip reef sharks consistently utilized these
areas of the west lagoon for many years, although they occasionally make brief
excursions to other locations within the west lagoon or to the east lagoon. Further
evidence for strong site fidelity is the fact that the majority of detections (mean 81 %) for
each shark were on one core receiver. Tag and recapture data of blacktips at Aldabra
Atoll also indicated high site fidelity as 81 % of recaptures occurred within 1 km of the tagging location (Stevens 1984). Similar levels of site attachment have been seen in both
juvenile and adult species of sharks from tropical islands and atolls (McKibben and
Nelson 1986, Chapman et al., 2005, Garla et al., 2006) although those species tended to
103 move over a larger area than the blacktips in the present study. Our data provides the longest time-frame over which site fidelity to a small area has been quantified for any species of shark. Similarly sized coastal sharks from sub-tropical and temperate bays, show less site attachment and perform extensive seasonal migrations, which are most likely attributed to the much greater seasonal variation in environmental conditions in those areas (e.g. Rechisky and Wetherbee 2003, Heupel et al., 2004). The repeated use of ledge habitats suggests that blacktip reef sharks at Palmyra are able to meet most of their energetic needs in relatively small areas (at least on certain ledges). The sharks also showed daily shifts in 50 % KUD’s within their home range, which has also been seen in juvenile lemon sharks and may be related to behaviorally mediated resource depletion
(Morrisey and Gruber 1993a, Brown et al., 1999).
Diel and tidal effects on behavior
Several shark species show increased rates of movement and size of activity space
at night suggesting nocturnal foraging (Nelson and Johnson 1980, McKibben and Nelson
1986, Garla et al., 2006). Blacktips at Palmyra show some degree of diel behavior, although there are intra-specific differences in the magnitude and consistency of the behavior between sharks. Although there are numerous explanations for diel behavior including feeding, predator avoidance, reproduction, and energetic advantages (Lowe and
Bray 2006), it is unclear as to what factors regulate this behavior for blacktips at Palmyra.
However, it is possible that the behavioral variation among individuals is a form of an
Evolutionary Stable Strategy (ESS) reducing intra-specific competition in predator dominated ecosystems, where competition for resources may be strong.
104 Tidal stage has been shown to effect shark behavior in several locations, with individuals moving onto previously exposed sand or mud flats at high tide to forage (e.g.
Nelson and Johnson 1980, Wetherbee et al., 2007). Both passive and active tracking suggest a tidal component to blacktip movements at Palmyra, although there were intra- specific differences in the magnitude of the response. Sharks had significantly lower rates of movement during the nocturnal flood tide than the ebb tide. The reduced rates of
movements for the sharks at Palmyra with the flood tides, corresponds with the influx of
cooler water and subsequent decrease in water temperature (up to 3 ºC, NOAA Coral
Reef Ecosystem Division). There are three potential explanations for reduced swimming speeds during this time period: 1) reduced metabolic rates caused by lower temperature,
2) reduced swim speeds due to foraging in small patches, 3) decrease in swim speeds from reduced search behavior and foraging. Explanation 1) is unlikely due to the fact that average ROM halved when the tidal cycle switched from the nocturnal ebb to the nocturnal flood period, yet based on the Q10 (as determined for other tropical species)
ROM should have decreased by 30 – 70 % (Carlson et al., 2004). Based on endogenous
rhythms in gastric motility and pH in captive blacktip reef sharks, it was previously hypothesized that individuals would preferably forage during times of low water temperature, as the natural delay in gastric motility following feeding (gastric accommodation) would coincide with times of increased water temperature
(Papastamatiou et al., 2007). The hypothesis would fit well with explanation 2, but presently either explanation 2) or 3) are plausible. Changes in rate of movement have also been shown to effect detection frequency by acoustic monitors (Topping et al.,
2006), which may also explain the tidal peaks in the VR2 detections. Previous studies of
105 sand flat associated fishes at Palmyra (e.g., bonefish, jacks) have shown that these fishes invade sand flats during flooding tides, but leave during falling tides via discrete corridors (Friedlander et al. 2007). It is likely that, at times, blacktip movements may be linked with these tidally driven prey migrations. Finally, qualitative data also suggests that blacktip reef shark pups and YOY show tidally-mediated movements. These sharks occupy areas (shallow sand-flats) that are inaccessible during low tide, hence some movements correlated with tidal flow must exist (similar behavior is seen in juvenile lemon sharks, Wetherbee et al., 2007).
Habitat utilization
Although there was no influence of shark length on home range area, there were ontogenetic shifts in habitat selection, with smaller sharks showing stronger selection for sand-flat habitats. Sand-flat habitats are characterized by shallow waters and would provide small sharks with protection from larger predators. Neonatal and YOY blacktip pups were only found in very shallow water, very close to shore. These smaller sharks could potentially have a number of predators including adult blacktip reef sharks, gray reef sharks (Carcharhinus amblyrhynchos), tiger sharks (Galeocerdo cuvier), and large teleosts. While the use of coastal bays as nursery areas is well documented in elasmobranchs (e.g. Heupel et al. 2007), far less is known about the use of small scale nursery zones (on the scale of meters) at atolls and islands (Garla et al., 2006, Wetherbee et al., 2007). There should be strong selection for the utilization of shallow (safe) habitats by shark pups in predator dominated ecosystems. Similarly, juvenile lemon sharks select for shallow inshore mangrove habitats or tidal pools to obtain protection from predation by larger sharks (Morrissey and Gruber 1993b, Wetherbee et al., 2007).
106 Larger sharks showed a clear habitat preference for reef ledges, often spending
their time patrolling back and forth along the reef ledge (also indicated by the relatively
high fractal values and the oblong shaped activity spaces used by the sharks). Use of
ledges as foraging sites appears to be a common feature of top level predators in both
terrestrial and marine systems (e.g. Heithaus et al., 2006, Phillips et al., 2004). Ledge or
edge habitat use has been seen in several elasmobranchs (e.g. Morrissey and Gruber
1993a, Rechisky and Wetherbee 2003, Heithaus et al., 2006), but only one other study
has quantified ledge use (Heithaus et al., 2006). We have conducted dives on the steep
ledges in the lagoons at Palmyra, and they appear to support a high abundance of
potential prey items, so we propose that blacktip reef sharks either obtain a higher forage
base over the ledges, or obtain greater encounter rates with prey (possibly prey moving
off the flats). The reduced swim speeds over ledge and sand-flat habitats are most likely a consequence of the sharks foraging in patches (see below) in these locations. Although
adult sharks spent less time than expected (based on available area) over sand-flats, all
adult sharks made brief excursions onto the flats. The increased rates of movement over
lagoon waters, is a consequence of straight-line swimming, suggesting that these habitats
are mainly used to transit between ledges and sand-flats. Large tiger sharks are
occasionally seen in the west lagoon, and it is also possible that adult blacktip reef sharks
reduce predation risk by avoiding deeper lagoon habitats.
Both active and passive tracking suggest that sand-flats within the west lagoon
may differ in quality, as sharks showed strong fidelity to the Banjos, Channel, and
Airport receiver areas, but much lower fidelity to the Nursery receiver even though these
locations are only a few hundred meters apart. All sharks tagged and actively tracked at
107 the Nursery’s ledge had left the area after 24 h, and could not be re-located there over
several days. Between 0 – 4 % of detections occurred at the Nursery’s receiver for
acoustically tagged sharks, even though four of the six sharks were tagged on the
Nursery’s sand-flats. The Nursery’s sand-flat does not have a coral ledge, unlike the
other flats, and therefore supports a lower biomass of potential prey items (per. observations, unpublished data). Therefore the ledge at Nursery’s, most likely represents an area of low habitat quality which may be driving the low level of site attachment shown by the sharks. Clearly, habitat quality is important for regulating the levels of site attachment, even over small spatial scales.
The lack of movement of sharks from the west to the east lagoon is also striking and may further be a function of habitat quality. However, the east lagoon serves some purpose for the life history of at least some individuals as indicated by the highly seasonal and synchronous migrations by two individuals. The reproductive cycle of blacktip reef sharks varies by location and can occur either annually (Porcher 2005) or every other year (Stevens 1984). The reproductive cycle of blacktips at Palmyra is unknown, but it is possible that the seasonal movements observed for some individuals may be related to mating behavior.
We were not able to quantify habitat use on the outer reefs at Palmyra, where conditions are very different from the inner lagoons. Although we observe blacktip reef sharks when diving on the outer reefs, the dominant predator is the grey reef shark, and therefore the ecological importance of blacktips may be reduced. However, there can be differences in behavior of sharks on ocean ledges versus lagoons (e.g. McKibben and
Nelson 1986).
108 Movement path structure and hunting strategy
Fractal analysis is a powerful tool in the study of animal movement paths although the majority of its application to date has focused on terrestrial animals (e.g.
Doerr and Doerr 2004, Nams 2005). However, the technique is gaining popularity for
use with marine animals and has been used to look at seasonal changes in movement path structure and to identify Area Restricted Search zones (Laidre et al., 2004, Tremblay et
al., 2007). The overall movement paths of blacktip reef sharks at Palmyra could not be
modeled with a correlated random walk, but instead they showed directed movements
within patches, while moving more randomly between patches. Fractal analysis was able
to detect patch use by blacktip reef sharks, with sharks using small sized patches (most of
a scale between 30 - 100 m, or approximately 3 – 17 % of the scale of the shark’s home
range length) on ledges and sand-flats. Movements within the 15 – 60 m scale range are scale-invariant suggesting that sharks move using a directed walk while in patches most
likely by orienting to ledge habitats (Nams 2006). The directed walk within patches
appears to be a common behavior amongst all sharks, as indicated by the narrow
confidence intervals at those scales. Shark movements became more tortuous at scales
between 60 – 107 m, which is most likely a function of more tortuous movements in
larger patches. The final domain occurs at scales > 107 m, with D rapidly increasing at
larger scales indicating that the sharks may use a highly correlated random walk to move
between patches (e.g. Doerr and Doerr 2004). The small home ranges utilized suggests
that blacktip reef sharks should have good information about the spatial distribution of
patches within their home range. However, patches are still likely to be spatially and
temporally dynamic, and theoretically, highly correlated random walks, leading to almost
109 straight movements, are thought to be the most efficient search strategy within a heterogenous environment (e.g. Zollner and Lima 1999, Philips et al., 2004). The repeated back and forth movements with a relatively straight movement path, should enable blacktip reef sharks to maximize search efficiency. Excursions and patch use on to the sand-flats are also made by some sharks, although at present we can only speculate that these are for foraging purposes. Short track time (maximum 3 d) is a limitation of active telemetry tracking, and it would certainly be desirable to analyze movement path structure over the scale of months to years. However, presently there is no other technique for obtaining high spatial resolution movement data from fish predators, especially those that confine their movements to small areas.
Clearly, our results can not be directly extrapolated to blacktip reef shark populations world-wide, but they do show how micro-habitat quality and quantity can effect movements, behavior and life history of top-level predators. The design of efficient marine reserves to conserve shark populations is particularly difficult due to the wide ranging movements of these animals. However, by using the analytical framework presented here, and by quantifying the scales at which sharks view and respond to their environment, we will be able to improve models used to predict population level dispersal at other locations. Furthermore, the mounting evidence for shifting baselines at predator depleted atolls (Sandin et al., 2008), is making it increasingly important to quantify predator behavior in the few remaining pristine atolls like Palmyra.
110 Chapter V
Distribution, size frequency, and sex ratios of blacktip reef sharks, Carcharhinus
melanopterus, at Palmyra atoll: a predator-dominated ecosystem
ABSTRACT
Understanding the dynamics of marine apex predators in unperturbed predator- dominated ecosystems is important for obtaining baseline information on predator/prey relationships and understanding ecosystem function. We conducted a study of blacktip reef shark Carcharhinus melanopterus, populations at Palmyra Atoll, a US National
Wildlife Refuge where we captured 254 individuals between March 2004 and November
2007. Blacktip reef sharks were the most abundant apex predator in the Palmyra lagoons
and were evenly distributed throughout the lagoons, except for lower Catch Per Unit
Effort (CPUE) at Banjos sand-flats in the west lagoon. Sharks ranged in size from 34 –
137 cm Total Length (TL, 94.9 ± 23.5 cm, mean ± 1SD), with females being caught at significantly larger sizes than males. Sex ratios did not differ from unity except for the
Channel sand-flats where the ratio was skewed towards females. Male sharks possessed calcified claspers when they reached 94 – 102 cm TL, suggesting that they were sexually mature, with 50 % of sharks caught having calcified claspers at 97 cm TL. Analysis of stomach contents (n = 14) revealed the first account of blacktip reef sharks foraging or scavenging on seabirds, with bird remains present in 29 % of stomachs. We recaptured
3.1 % of 193 sharks dart tagged after 5 – 166 d at liberty, with no evidence of movements between lagoons, suggesting a large population size and site fidelity to specific lagoons.
Due to the potentially high numbers of sharks at the atoll, blacktip reef sharks may have a
111 strong regulatory function on the marine ecosystem at Palmyra. The smaller sizes of blacktip reef sharks at Palmyra compared to other locations, suggests that this population may be growth limited, possibly due to intra-specific competition associated with high population densities.
INTRODUCTION
Marine apex predators are generally the first trophic group to be impacted by fishing and other anthropogenic influences (Jennings and Kaiser 1998, Jackson et al.
2001). Many species of shark are apex predators and due to their slow growth and other life history characteristics, population declines worldwide from over fishing are becoming apparent (e.g. Stevens et al., 2000, Meyer and Worm 2003). Consequently, areas that are closed to fishing or in remote locations are often characterized by a higher biomass of apex predators, in particular sharks (Friedlander and DeMartini 2002, Sandin et al., 2008, DeMartini et al., 2008). It is becoming increasingly important to quantify the ecological impacts of sharks in un-fished, pristine ecosystems, as this will provide baseline data which can help management decisions in areas subjected to fishing pressure. Understanding the ecological impacts of a population of predators requires knowledge of the predator’s diet and feeding habits, movements and habitat utilization, distribution, population size and age and sex structure.
Palmyra is an uninhabited atoll located in the central Pacific Ocean and has been a
US National Wildlife Refuge since 2001. Due to Palmyra’s protected status and remote location, it supports a healthy population of marine apex predators which makes up to
35% of the fish biomass (DeMartini et al., 2008, Sandin et al., 2008). A large shark population exists at Palmyra, and the dominant species inside the lagoons and over the
112 sand-flats is the blacktip reef shark, Carcharhinus melanopterus (Hobson 1963,
Friedlander et al., 2007, Papastamatiou Chapter 4). Blacktip reef sharks are a common
species on shallow coral reefs of the Indo-Pacific, where they are often the most abundant
species of shark (Stevens 1984, Compagno et al., 2005). Dietary analysis in other
locations suggests that blacktip reef sharks are tertiary predators and may therefore exert
top-down control on some coral reef ecosystems (Lyle and Timms 1987, Cortes 1999).
In order to describe the population of blacktip reef sharks at Palmyra, we conducted a
population study of sharks over a three year period. Our specific goals were to: 1)
describe the distribution of sharks throughout the Palmyra lagoons, 2) quantify sex ratios
for sharks and how these may vary spatially, 3) quantify the size frequency distribution of
sharks in the lagoons and 4) determine the size at which male sharks possess calcified
claspers (as an indicator of sexual maturity). Although we had limited data due to
collecting restrictions, we also obtained some information on shark diet.
MATERIALS AND METHODS
Study location
Palmyra Atoll (N 5°53’, W 162°05’) is located in the central Pacific and is part of the Line Island chain (Fig.17). The atoll is approximately 12.5 km in length, has an area of 27.6 km2, and consists of two large lagoons (west and east) that are connected by a small channel. The lagoons are separated by a deteriorating road which was constructed by the US military during their occupation of Palmyra during WW2. A large channel (2 km length x 5 m deep) connects the west lagoon to the outer reefs (Fig.17). Water in the lagoons reaches a maximum depth of 50 m with a mud/sand bottom causing low
113 Figure 17. Location of the Line Islands in the Pacific Ocean (box), and Palmyra’s
location within the Line Island chain. Aerial image shows the location of the west (W)
and east (E) lagoon, as well as fishing locations (circles). Sand-flats are B (Banjos), C
(Channel), N (Nursery), and S (Sixes).
114 B Ba E Ba W C W MDE A J E Co Ed. In. Ed. S B N N S C
115 visibility. The lagoons are also connected to extensive sand-flats that are exposed during extreme low tides. The outer reefs of Palmyra consist of a steep slope with high coral cover and good water visibility. Palmyra is located in the Inter-tropical Convergence zone and receives up to 500 cm of rainfall annually. As a consequence, terrestrial habitat is rain-forest and the atoll is a nesting ground for several species of seabird (Handler et al., 2007). Palmyra has been a U.S. National Wildlife Refuge since 2001, with only a small group of up to 17 scientists and refuge staff inhabiting the atoll at any one time.
Sampling
We fished for blacktip reef sharks at select location in both the west and east
lagoons at Palmyra. Sharks were caught on hand-lines with barb-less hooks and then
brought along-side the boat, where they were restrained, inverted, placing them into tonic
immobility, a trance like state. All sharks were measured (Total Length (TL), Fork
Length (FL), Pre-caudal Length (PCL)), sexed, and a numbered dart tag applied just
below the dorsal fin through the epaxial muscle. For more accurate underwater
identification, a small number of sharks were also dorsally tagged using standard Roto-
tags. We estimated sexual maturity in males by measuring clasper length and
qualitatively determining the degree of calcification of the claspers. Generally, calcified
claspers are considered a sign of sexual maturity in male sharks, although it should be
noted that in some cases there can be a lag between calcification of claspers and testicular
development. Sharks were then released and all animals swam away vigorously.
Blacktip reef shark neonates and Young-Of-Year (YOY) utilize very shallow
sand-flat habitats (Papastamatiou Chapter 4). We used a 20 m seine net to sample pups at
116 these locations. The pups were herded into the net which was positioned perpendicular to the shoreline. Due to their small size, we also weighed a small number of blacktip pups
in addition to measuring them. Based on the two age/length classes apparent in the size
frequency distribution, we separated sharks into two classes, < 65 cm TL and those > 65
cm TL. Shark total length was log transformed to conform to the assumptions of
normality and homogeneity of variance, and compared between male and female sharks
separately for the two age classes, using a Students t-test.
Fishing was conducted at three sites in the west lagoon (edges of Banjos, Nursery,
and Channel sand-flats) and one site in the east lagoon (edge of Sixes sand-flat, Fig.17).
For each location, we calculated Catch Per Unit Effort (CPUE) defined as the number of
sharks caught per hook per hour. Due to logistical difficulties associated with getting to
Palmyra Atoll, we were not able to sample at all times of the year, and consequently we
could not quantify seasonal changes in CPUE. We therefore combined CPUE data from
multiple fishing trips for each location. In order to meet the assumptions for the
homogeneity of variance, all CPUE data were square root transformed. We then
performed a one-way ANOVA followed by a Tukey’s HSD (Honestly Significant
Differences) Post Hoc test to examine pair-wise differences between locations. We
compared sex ratios for blacktip reef sharks for the whole atoll and then by fishing
location. In each case, sex ratios were compared against an expected 1:1 ratio using a
Chi-squared test for Goodness of Fit (X2). We determined the total length at which 50 %
of male sharks had calcified claspers by fitting a logistic curve to the clasper length/state
data where P = 100 / (1+exp(a-bTL)), where P = percentage of sharks with calcified
claspers at x cm TL, and L50 =(-a/b).
117 We obtained stomach samples from a small number of sharks using a non-lethal
gastric evacuation method. While in tonic immobility, a lubricated PVC pipe was gently
inserted into the stomach via the buccal cavity. Approximately 0.5 L of seawater was
poured down the pipe into the stomach and the shark was quickly brought into the boat
and tipped head down so that stomach contents exited the PVC pipe and collected into a
bucket. The pipe was then removed and the shark was released.
RESULTS
In total, we caught and measured 254 blacktip reef sharks between March 2004
and November 2007. We fished at all times of the day at every location, although no
fishing was done at night. In addition, while fishing we caught eight whitetip reef sharks
(Triaenodon obesus), 4 grey reef sharks (Carcharhinus amblyrhinchos) and four giant
trevally (Caranx ignobilis). Male blacktip reef sharks ranged in total length (TL) from 34
– 119 cm (93 ± 20 cm, Mean ± 1SD) and females from 37 – 137 cm (96 ± 26 cm, Fig.
18). There were two apparent size classes at Palmyra, one for juvenile sharks with a
mode of 50 cm and another for sub-adult and adult sharks with a mode at 110 cm
(Fig.18). For the sub-adult and adult sharks, females (107 ± 14 cm) were caught at larger sizes than males (100 ± 10 cm, t = 3.83, p < 0.0001). Juvenile male sharks (45 ± 5 cm)
did not differ in size from females (47 ± 6 cm, t = 0.70, p = 0.49). A length- weight
regression was constructed for juvenile sharks, which was best described with a linear
relationship where Mass (g) = 28.4 (TL)-845.6 (F = 221.7, d.f. = 20, r2 = 0.92, p <
0.0001, Fig. 19). We also describe length-length regressions between PCL, FL and TL for this species (Table 8).
118 Figure 18. Size frequency histogram for 254 blacktip reef sharks caught at Palmyra atoll.
Black bars are for male sharks, grey bars are for females.
119 40
30
20 Percentage 10
0 20 40 60 80 100 120 140 160
Total Length (cm)
120 Figure 19. Length-weight regression for juvenile blacktip reef sharks at Palmyra atoll. A linear regression, Mass (g) = 28.4 (TL)-845.6 (r2 = 0.92, p < 0.0001), best described the data.
121 1000
800
600
Mass (g) Mass 400
200
0 30 35 40 45 50 55 60
Total Length (cm)
122 Table 8. Linear length-length relationships for blacktip reef sharks at Palmyra Atoll.
Parameters are for the model Yi = b0 + b1Xi. PCL = Pre-caudal length (cm), FL = Fork length (FL), TL = Total length (cm). Standard errors of the means are in parenthesis.
2 X Y b0 b1 r
PCL TL 4.25 (0.72) 1.25 (0.01) 0.98
TL PCL -2.18 (0.60) 0.79 (0.01) 0.98
FL TL 2.96 (0.80) 1.15 (0.01) 0.98
TL FL -1.00 (0.71) 0.85 (0.01) 0.98
PCL FL 1.48 (0.39) 1.08 (0.01) 0.99
FL PCL -0.91 (0.37) 0.92 (0.004) 0.99
123 CPUE varied among fishing locations in the west and east lagoon (ANOVA, F3, 31
= 7.88, d.f. = 34, p < 0.0001, Table 9). The CPUE at Banjos (0.512 ± 0.307,
sharks/hook/hour) was significantly lower (p<0.05) than the CPUE at all other locations
but there were no differences in CPUE between the ledges at the Channel, Nursery or
Sixes sand-flats (Table 9).
We caught 125 males and 129 females at Palmyra Atoll, which did not differ
significantly from unity (X2 = 0.06, p > 0.05). Sex ratios at individual locations within
the west and east lagoons also did not differ from unity, with the exception of the
Channel sand-flats (Table 9). At the Channel sand-flats, we caught 42 females and 13
males (3.2:1), which differed significantly from unity (X2 = 12.79, p < 0.01), during
fishing trips in September and November 2007.
Based on the logistic curve, 50 % of male blacktip reef sharks had calcified
claspers at approximately 97 cm TL (Fig.20). The smallest male with calcified claspers
was 94 cm TL, while the largest shark with non-calcified claspers was 102 cm TL (Fig.
20a). If we assume that male blacktip reef sharks are sexually mature at 97 cm TL, then
56.8 % of males caught were sexually mature.
We performed gastric evacuations on 14 sharks of which 7 (50 %) had empty stomachs. Of the remaining sharks, we found remains of teleosts (scales and eye lenses) in three sharks. One shark had unidentified algae in its stomach and one had rat fur
(Ratus ratus). Four sharks (29 %) had seabird remains in the stomach, one of which
included the wing of a sub-adult or adult red footed boobie (Sula sula). The smallest
shark containing bird remains was 103 cm TL.
124 Table 9. CPUE (sharks/hook/h) and sex ratios for sharks caught on sand-flats within the
west (Channel, Banjos, Nursery) and east (Sixes) lagoons at Palmyra Atoll. P values for
X2 test are given. All values in bold are statistically significant.
Location CPUE Males Females Ratio X2 P value
Channel 1.59 ± 0.37 15 42 1:3.2 12.79 <0.01
Banjos 0.51 ± 0.31 9 8 1.1:1 0.06 >0.05
Nursery 1.30 ± 0.72 61 47 1.3:1 1.81 >0.05
Sixes 1.69 ±0 .51 29 20 1.5:1 1.65 >0.05
125 Figure 20. a) relationship between total length and clasper length for male blacktip reef sharks at Palmyra atoll. Black circles are calcified claspers, while white circles are non- calcified. b) percentage of male sharks with calcified claspers. A logistic curve has been fitted to the data. The line represents the size at which 50 % of male blacktip reef sharks have calcified claspers.
126 a 20
18
16
14
12
10
8
6 Inside clasper length (cm) Inside clasper length
4
2 60 70 80 90 100 110 120 130 Total Length (cm)
b
100
80
60
Percent 40
20
0
60 80 100 120 Total Length (cm)
127 We recaptured 6 (3.1 %) sharks out of the 193 sharks externally tagged (144 west
lagoon, 49 east lagoon). Distance from original tagging location varied from 0 – 1.6 km
with a median of 0 km. All sharks were recaptured in the same lagoon they were originally tagged in after 5 – 166 d (median 65 d) at liberty. The shark at liberty for 166
d had grown 2 cm in length. One of the female sharks caught in September 2007 had
appeared pregnant with a girth of 49 cm (as measured directly behind the pectoral fins).
When she was recaptured in November her girth had decreased to 43 cm.
DISCUSSION
As measured by fishing success, blacktip reef sharks are the most abundant top-
level predator in the lagoons and over the sand-flats of Palmyra Atoll. Although they are
also present on the outer reefs, there they are replaced in abundance by the grey reef
shark (Carcharhinus amblyrhinchos) and the red snapper (Ljutanus bohar) (Demartini et
al., 2008, Per. Observ.). Adult female blacktip reef sharks reach a larger size than males;
as we did not catch any males larger than 119 cm TL, while 14 % of females were > 120
cm in length. Similarly, female blacktip reef sharks reached larger sizes than males at
both Aldabra atoll, Indian Ocean (Stevens 1984) and in Northern Australia (Lyle 1987).
There is some spatial segregation of sharks at Palmyra, with adults showing selection for
sand-flat ledges (the steep slopes which mark the transition from sand-flats to deeper
lagoons), smaller sharks selecting sand-flats, and YOY and pups utilizing very shallow
sand-flat habitats close to the shoreline, presumably because this provides refuge from
predators (Papastamatiou Chapter 4). There was no difference in size between the sexes
of newborn and YOY sharks, which indicates that female sharks either obtain faster
128 growth rates or live longer than males as adults. Although there appeared to be an absence of sharks in the intermediate size range between juvenile and adult sharks (see size frequency histogram), this may be related to gear selectivity. Adult sharks were caught using hook and line, while pups were caught in seine nets. Hence, intermediate sized sharks may not have caught using those two fishing techniques.
Blacktip reef sharks at Palmyra are smaller than those in other locations with the largest shark we caught being 137 cm. While larger sharks could be distributed on the outer reefs, we have conducted dive surveys around the entire atoll and have never seen any evidence of larger individuals. Sharks at Aldabra atoll reach 140 cm (Stevens 1984) while those elsewhere in the Indian and Pacific Ocean, can reach at least 160 – 180 cm
TL (Bass et al., 1973, Compagno et al., 2005). Furthermore, we measured shark pups as small as 34 cm TL at Palmyra, which is considerably smaller than the size at parturition in Aldabra and Northern Australia, where blacktip reef shark pups are born between 48 –
50 cm TL (Stevens 1984, Lyle 1987). However, this may be more a characteristic of atolls in the Pacific Ocean versus continental locations because free-swimming blacktip reef sharks as small as 33 cm TL have been found in the Marshall Islands (Bonham
1960). We hypothesize that blacktip reef sharks are growth limited at Palmyra due to high levels of intra-specific competition resulting from high population density (see below).
Although there is some age-structured spatial partitioning, adult sharks still make excursion on to the sand-flats, presumably to forage, so there will be some competition for resources between age classes (Papastamatiou Chapter 4). Theoretically, spatial and dietary overlap between age classes can cause high levels of exploitation competition
129 (Polis 1988). Blacktip reef sharks in other areas show high levels of dietary overlap between small and large individuals (e.g. Stevens 1984), so we further hypothesize that the age-structure at Palmyra in conjunction with the small size of the atoll (increasing the chance of spatial overlap between age classes) is driving the high levels of intra-specific competition. The small sizes of sharks at Aldabra were also attributed to high levels of intra and inter specific competition, causing the blacktips to be food-limited (Stevens
1984). The growth rate we calculated for one shark at Palmyra (5cm y-1) was very
similar to the rates observed in blacktip reef sharks at Aldabra atoll (Stevens 1984).
The only area where the sex ratio differed from unity was around the Channel
sand-flats where the numbers were skewed towards females. Sexual segregation has
been observed in a number of species of elasmobranch (Springer 1967, Klimley 1987,
Sims et al., 2001). Several explanations exist, including females moving to more
productive areas where they can attain faster growth rates, and seeking refuge from males
and subsequent energy demanding mating activities (Klimley 1987, Sims et al., 2001).
The skewed sex ratio at Palmyra may be seasonal with females moving to this area for
parturition, although this seems unlikely based on the location of habitats utilized by
neonate sharks (Papastamatiou Chapter 4).
The reproductive cycle of blacktip reef sharks at Palmyra is unknown. At
Aldabra atoll, female blacktip reef sharks are thought to breed every other year with a 10
– 11 month gestation period (Stevens 1984), while sharks in Moorea reproduce annually
with a similar gestation period (Porcher 2005). It was theorized that high levels of intra
and inter-specific competition could be causing the alternate year breeding cycle at
130 Aldabra atoll (Stevens 1984). We are currently developing the use of non-lethal techniques to assess the breeding cycle of female sharks at Palmyra Atoll.
Male sharks at Palmyra may reach sexual maturity between 94 – 101 cm TL based on calcification of claspers, with 50 % of the population having calcified claspers by 97 cm. Assuming clasper calcification is strongly correlated with testicular maturity, this size of maturity is larger than the size of maturity for sharks in Northern Australia, where all individuals were mature by 95 cm TL (Lyle 1987), but smaller than those sharks at Aldabra, where maturity is reached at about 105 cm TL (Stevens 1984). The large variation in size at maturity for male sharks suggests that some other factors may be having an influence, such as intra-specific differences in diet or foraging locations.
Alternatively, there may be differences in productivity between small atolls and coastal waters.
There was no significant difference in CPUE of sharks between the west and east lagoons, suggesting similar abundance. However, within the west lagoon, there was lower CPUE at the Banjos sand-flats than the flats at Nursery and the Channel. There is evidence that ledges of different sand-flats vary in habitat quality, with the Nursery ledges supporting a lower biomass of potential prey species than ledges at Banjos and the
Channel (Friedlander et al., 2007, per. Obs.). Furthermore, sharks show stronger site fidelity to Banjos and the Channel ledges than they do at Nursery, which may suggest that the Banjos ledges support a small number of site-attached sharks (Papastamatiou
Chapter 4). However, when quantifying CPUE using fishery dependent methods, it is important to recognize that CPUE is not always equivalent to abundance. For example, while chumming at Banjos flats, the current primarily transports the odor corridor
131 towards land. The sand-flats at Nursery, Channel and Sixes have a greater area over
which the odor can be transported (see Fig.17). Hence the differences in CPUE may reflect differences in physical and environmental characteristics between Banjos and the other fishing locations.
Other diet studies have found blacktip reef sharks to be primarily piscivorous, but they also consume mollusks, crustaceans and, in Northern Australia, sea snakes (Stevens
1984, Lyle 1987, Lyle and Timms 1987, Salini et al., 1992). Although we also found teleost remains, we provide the first documentation of predation or scavenging on seabirds by blacktip reef sharks. Palmyra is a breeding ground for a number of seabird species including, brown (Seula leucogaster) and red-footed boobies (Sula sula). While,
we could be simply documenting scavenging, we have seen active predation of sharks on
seabird chicks that have fallen out of their nests overhanging the water edges. The
trophic level of blacktip reef sharks at Palmyra may therefore be elevated, although
currently it is unclear how much predation on birds occurs, or if shark induced mortality
on seabirds is compensatory or additive.
The low recapture rate suggests a large population of blacktip reef sharks at
Palmyra. All recaptures occurred in the same lagoon as original tagging, which agrees
with a concurrent telemetry study suggesting strong site fidelity to lagoons with limited
movements between lagoons (Papastamatiou Chapter 4, unpublished data). The fact that
sharks have long residence times in the west lagoon (Papastamatiou Chapter 4) provides
further evidence that the low recapture rate is due to a large population of sharks. High
mortality associated with tagging is unlikely to explain the low recapture rate, as
indicated by results from the telemetry study in which no tagging mortality was observed.
132 Due to logistics associated with working in a remote location, we were not able to
perform a rigorous year-round sampling of sharks at Palmyra. Nevertheless, our study
provides important information on the ecology and life-history of a shark population at a
non-fished atoll. The apparently high numbers of blacktip reef sharks in the Palmyra lagoons suggests that they may regulate the trophic food web by some form of top-down control. However, the importance of understanding the social system and age structure of apex predators in healthy ecosystems is also highlighted by the small spatial scale over which abundance and sex ratios can vary. How are mating and parturition activities regulated in an atoll that only measures 12 km in length? Future studies will aim to determine if sharks at the atoll are indeed growth limited and if this is a common feature of apex predators in pristine, healthy environments.
133 Chapter VI
Habitat influences residence time and foraging success of blacktip reef sharks at a
predator dominated coral ecosystem
ABSTRACT
Intra-specific competition is thought to be a strong regulator of apex predator
populations. Understanding the extent of this regulation requires determining how
habitat selection and movement patterns can influence foraging success, particularly in
areas where high levels of intra-specific interference are likely to exist. We used acoustic
telemetry and stable isotopes to compare movement patterns, macro-scale habitat use,
trophic position and foraging success of blacktip reef sharks, Carcharhinus
melanopterus, living in two lagoons (east and west) at Palmyra Atoll, a US National
Wildlife Refuge. Most sharks showed diel and tidal movements, although there were
individual differences in the extent and consistency of these behaviors. Sharks showed
low levels of migration between lagoons, and individuals in the west lagoon had longer
residence times (median 280 d) than those in the east lagoon (median 63 d).
Furthermore, sharks in the west lagoon had higher body condition indices than those in
the east lagoon, and displayed no relationship between total length (TL) and relative 15N and 13C concentrations. For sharks in the east lagoon, TL had a positive relationship with
15N and a negative relationship with 13C. These results suggest that sharks in the west lagoon have greater foraging success than those in the east lagoon, and subsequently longer residence times. The trophic relationships of sharks in the west lagoon were also different from those in the east, possibly due to sharks in the west lagoon having easier access to a greater variety of habitats. Blacktip reef sharks do not conform to an ideal
134 free distribution (IFD) in Palmyra lagoons, which matches predictions from theoretical models where low predator migration rates between patches causes deviations from the
IFD, and may promote predator-prey stability due to asynchronous cycles between sharks and prey in each lagoon.
INTRODUCTION
Optimal foraging theory predicts that animals make behavioral decisions when foraging that optimize the net acquisition of energy (e.g. Pyke et al., 1977, Stephens and
Krebs 1986). Maximizing net energy acquisition requires decisions by the predator on diet choice, foraging tactics, habitat selection and movement patterns, and a large number of mathematical models have been developed to predict or explain predator behavior (e.g.
Fretwell and Lucas 1970, Stephens and Krebs 1986, Lima 2002). However, there may be more than one optimal strategy, with the frequency of each strategy in a population reaching a stable endpoint and being known as an evolutionary stable strategy (ESS).
Applying optimal foraging theory to marine apex predators is particularly difficult due to the relatively low abundance of these animals and generally long ranging movements occurring in a concealing medium. However, within populations of marine apex predators, there can be intra-specific differences in movement patterns and foraging strategies (e.g. Estes et al., 2003, Austin et al., 2004, Whitehead and Rendell 2004).
Furthermore, one study with sperm whales demonstrated that not all movement and foraging strategies are ‘equal’ with inter-individual differences in foraging success
(Whitehead and Rendell 2004). Many species of sharks are apex predators, yet nothing is currently known of how intra-specific differences in movement patterns and habitat
135 selection affect foraging success. Developing predictive models of shark movements and
dispersal requires measurement of movements and habitat choices and an understanding
of how the animal’s movements and foraging strategies respond to habitat, and also how
habitat selection may influence fitness or foraging success. With the worldwide decline
in shark populations due to over-fishing (e.g. Myers and Worm 2003), it is becoming
increasingly important to design effective Marine Protected Areas (MPA’s) that can
incorporate the correct habitat to conserve shark populations.
It has been hypothesized that intra-specific competition may be an important
regulator of apex predator populations (Estes et al., 2003). Consequently, a shark’s
habitat selection and foraging success could be a function of the predator population
density, and it is necessary to quantify these parameters in healthy populations under
‘base-line’ conditions. There are relatively few pristine marine habitats with un-fished
shark populations, but notable exceptions include island or atoll scale MPA’s (e.g.
Friedlander and DeMartini 2002, Sandin et al., 2008). The effect of shark removal in
some fished areas has been suggested to have caused trophic cascades and changes in
food web structure, making atoll scale MPA’s ideal locations for quantifying effects of
habitat and movements on foraging success of shark populations (e.g. Sandin et al.,
2008).
Palmyra Atoll is located in the Central Pacific Ocean, and is a US National
Wildlife Refuge, with a large population of sharks that makes up to 35 % of the reef fish
biomass (Sandin et al., 2008, DeMartini et al, 2008). Within the lagoons and over the
sand-flats of Palmyra, the dominant predator is the blacktip reef shark, Carcharhinus melanopterus (Friedlander et al., 2007, Papastamatiou Chapter 5). Palmyra is composed
136 of two large lagoons and active tracking of blacktips suggested that they show strong site fidelity to core sand-flats (Papastamatiou Chapter 4). Furthermore, deployment of a small number of long term transmitters, as well as mark and recapture data, indicated that there were low rates of movement of sharks between the two lagoons (Papastamatiou
Chapter 4,5). Due to differences in area, flow rates, and temperature between lagoons, we hypothesized that residence time, and movement patterns may differ for sharks in the two lagoons. We also hypothesized that differences in movement patterns would enable sharks from both lagoons to have similar levels of foraging success. We used a combination of acoustic telemetry and stable isotopes to determine how residence times and movements varied between sharks within the two lagoons, and how this correlated with foraging success and trophic position. Specifically, we aimed to 1) determine levels of inter lagoon movements, 2) determine differences in residence times between sharks in the two lagoons, 3) use a body condition index to quantify differences in foraging success between lagoons, 4) use stable isotopes to quantify difference in trophic relationships between lagoons.
MATERIALS AND METHODS
Study site
Palmyra Atoll is located in the northern Line Islands (N 5°53’, W 162°05’)
approximately 1000 km south of the Hawaiian Islands (Fig.21). The atoll consists of two
lagoons, which are connected by a small shallow channel, which experiences strong tidal
currents. The west lagoon has a maximum depth of 50 m, an area of 3 km2, and is
connected to the outer reef by a 5 m deep, 1.7 km long channel. The east lagoon has a
137 Figure 21. Map of Palmyra Atoll, and its location within the Line Island chain. Black circles in aerial image represents detection radius of VR2 receivers deployed throughout the lagoons (radius 300m). VR2’s are Outer Channel (C), Barge (B), Eddies (Ed.), Inner
Eddies (In. Ed.), West Banjos (Ba W), East Banjos (Ba E), Nursery (N), Airport (A),
Jenns (J), Midchannel (M), Sixes (S), Downeast (DE), Cookies (Co). Sixes, Downeast and Cookies are all located in the east lagoon; all other receivers are in the west lagoon.
138 N
Ba# E # # Ba# W # MDE # J # # A Co # In. Ed. Ed. #S #B #N
# C 400 0 400 800 1200 Meters
139 maximum depth of 30 m and an area of 1 km2, but is only connected to the outer reefs via
shallow sand-flats. Both lagoons consist of a mud/silt benthic substratum, and
consequently have low water visibility. Extensive sand-flats can be exposed to air during
extreme low tides, and connect to the lagoons by steep coral ledges. The outer reefs are
characterized by steep slopes, high coral cover and good water visibility. Palmyra is
located in the inter-tropical convergence zone and receives up to 500 cm of rain annually,
and consequently terrestrial habitat is rainforest and is breeding habitat for a large
number of seabirds (Friedlander et al., 2007). Palmyra has been a US National Wildlife
Refuge since 2001, with only a small group of up to 17 staff and researchers inhabiting the atoll at any one time. Consequently, anthropogenic impacts at Palmyra have been maintained at very low levels.
Long-term movements
We surgically implanted 49 blacktip reef sharks with long life acoustic transmitters. Sharks were caught in both the west and east lagoon using barb-less hooks
and brought alongside the boat, where they were restrained and turned on their backs,
placing them in tonic immobility, a trance like state. A small incision was made through the abdominal wall, the transmitter inserted into the body cavity, and a single suture was
used to close the wound. The shark was then measured, sexed and released. We tagged
sharks with Vemco V8SC-2L-R04K transmitters (8mm diameter x 20 mm length, Nova
Scotia, Canada) in 2004/2005, and V9-2L-R04K transmitters (9mm x 20 mm) in
2006/2007 (battery life approximately 1 year). A small number of sharks were also fitted
with more powerful V16 transmitters (16mm x 20 mm), as these have a battery life of up
to 3 years. Transmitters emit pulse trains of approximately 3.5 sec in duration at
140 frequencies of 69 kHz. A randomized delay of 30 - 90 sec occurs between pulse trains that reduces the chance for acoustic collisions between pulse trains from other neighboring transmitters. Transmitters produce pulse trains with a unique code so that individual sharks can be identified.
Transmitters were detected by a network of 13 underwater listening stations, strategically placed throughout the Palmyra lagoons (Fig.21). Underwater listening stations are omni-directional Vemco VR2 receivers, which can record the presence of transmitters, every time a shark swims within range (approximately 300 m). For each valid acoustic detection, the VR2 receivers record the time, date and transmitter number.
Receivers were suspended below floats anchored to the mud/silt lagoon floor by sand- screws. Receivers were typically 5-10 m below the surface depending on the location.
Receivers were retrieved and downloaded every 6 – 9 months, then re-batteried and redeployed. Eight VR2’s were deployed in 2004, and an additional five were added to the array in 2006 (13 total).
Due to the slightly different transmitter specifications and number of VR2’s deployed during each tagging season, we analyzed data separately for sharks tagged in
2004/2005 from those tagged in 2006/2007. For each shark, we determined the total number of detections and the duration over which sharks were detected. We determined what percentage of detections occurred at each receiver for each shark. We square root transformed the percentage detections at each receiver for each shark and generated a
Bray-Curtis similarity matrix between sharks. We then used a one-way ANOSIM test
(Primer ver. 5) to quantify spatial overlap between sharks of the east and the west lagoon, as well as between male and female sharks for each lagoon separately. ANOSIM
141 calculates overlap between factors and compares them against 999 random permutations to generate a Global R statistic (-1 > R < 1) and a p value. Statistical significance in the
ANOSIM test (α = 0.05) indicates that factors did not overlap with each other. Non- metric multidimensional scaling ordinations (nMDS) were used to graphically demonstrate any difference in space utilization.
There were not enough sharks tagged in 2004 to statistically compare male and female spatial overlap using an ANOSIM test. Therefore, we calculated individual levels of overlap between sharks tagged in 2004 using a null model generated in EcoSim (ver.
7, Gotelli and Entsminger 2001). A null model compares calculated overlap between individuals against simulated overlap calculated from randomly reshuffled resource states
(Connor and Simberloff 1979). The resource states used in the model were percentage detections at each receiver, and we used the RA3 algorithm in EcoSim with 1000 simulations (Gotelli and Entsminger 2001). Overlap between individuals was calculated using the Pianka index, which ranges between 0 (no overlap) and 1 (complete overlap).
Statistical significance from the null model indicates that individuals showed strong overlap with each other.
We used time series analysis to detect diel or tidal movement patterns for each shark. We calculated the number of detections that occurred during each hour of every day for the duration that each shark was detected. We then used a Fast Fourier
Transformation (FFT) to search for cyclical patterns in the data set. The FFT converts time series data into frequencies and searches for sinusoidal patterns, which can be identified as peaks in a power spectrum. A Hamming smoothing function was applied to the data before running the FFT, which helps to reduce biologically meaningless peaks in
142 the power spectrum (Shepard et al., 2007). All FFT analysis was conducted using
Statistica (ver. 6, Statsoft).
Stable isotopes and body condition indices
The composition of heavy isotopes in an animal’s tissues reflects the
concentration in its food and the isotopic signature of the primary producers in the
ecosystem. During isotopic fractionation, when 15N and 13C are preferentially incorporated into the predator’s tissues, changes in the ratio of heavy: light isotopes relative to those of the prey occur. Specifically, the ratio of 15N: 14N increases by
approximately 3.4 ‰ with each successive trophic level (Graham et al., 2006).
Therefore, the 15N ratio is an indicator of a predator’s trophic position in the food web,
while the 13C: 12C ratio highlights the source of carbon for the primary producers at the
base of the food chain from which the predator is feeding (e.g. coastal or pelagic). We caught sharks in both lagoons as described above. We made a small incision in the flank of the shark and used a biopsy sampler to remove a small piece of epaxial white muscle tissue. The biopsy was taken below the dermal tissue, so the sample was muscle and did not include any skin. Samples were frozen until they were processed at the stable isotope laboratory at the University of Hawaii at Manoa. Samples were dried in a 60 ºC drying oven for at least 48 h or until the sample was completely dried out, and then ground into a fine powder and weighed out into micro sampling dishes. We used a carbon-nitrogen analyzer (Finnigan ConFlo II/Delta-Plus, Bremen, Germany) to determine the relative ratio of 15N and 13C in each sample. Values were presented as ‰, relative to the ratio of
13 15 heavy: light isotopes for standards of V-PDB and atmospheric N2 for C and N
respectively. Measurement accuracy was improved by also comparing measurements
143 during each analysis against a glycine standard. There has been considerable debate over
the necessity for lipid extraction before stable isotope analysis. However, a recent study of tropical tunas and sharks found no effect of lipid extraction on isotope values (Graham et al., 2006, Graham unpublished data). In addition, all the sharks sampled in this study had C : N ratios of 3.36 ± 0.09 (mean ± 1SD, range 3.26 – 3.60) suggesting that tissues were primarily composed of protein, with little variation between individuals. We used a
General Linear Model (GLM) to analyze isotope data, with δ15N and 13C as the dependent
variable, and shark length (TL), location (east or west lagoon) and sex as independent
variables. Interaction functions between TL and location and TL and sex were included
in the model. Shark length was also set as a covariate. Although we wanted to analyze
seasonal changes in isotopic signatures, Palmyra’s remote location prohibited us from
sampling year round. Therefore, all samples were collected in September through
November in 2006 and 2007.
Body condition indices were calculated for sharks in both lagoons by dividing shark total length by girth (defined as the perimeter of the animal measured directly behind the pectoral fins). We used a GLM as described above to determine the effects of shark length, sex and location on body condition index. In this case, shark total length was normalized using a square root transformation.
RESULTS
Long-term movements
We tagged 49 blacktip reef sharks between March 2004 and May 2007. We
tagged 16 sharks with V8 transmitters in March 2004 and 2005, and 29 sharks with V9
144 transmitters during October 2006 and May 2007 (Table 10). Four sharks were also tagged with more powerful V16 transmitters (Table 10). We detected 35 individuals (71
%) for durations of 9 – 1,160 d (median 131 d, Table 10). Tagged sharks were detected at all receivers within the array, although there were inter-individual differences in the
number of VR2’s visited by each individual shark (Fig. 22). The total number of detections for each shark ranged from 15 – 150,348 (median 686).
Visual examination of the data shows that Sharks in the west and east lagoon were
primarily detected in the lagoons in which they were tagged. Exceptions were sharks #83
and #80 that were tagged in the east lagoon, but all detections (3191 and 1404
respectively) were from receivers in the west lagoon (Fig.22). Due to the very low
numbers of detections of sharks in adjacent lagoons (see below), it seems highly likely
that these were west lagoon sharks making short movements to the east lagoon when they
were caught, and were consequently removed from all other analysis. Of the 32 sharks
for which data was obtained, 15 (47 %) showed movements between lagoons, although
these inter-lagoon movements were brief and did not occur often (Table 11). Of the 20
sharks tagged in the west lagoon, 8 (40 %) moved between lagoons, while of the 12
sharks tagged in the east lagoon, 4 (33 %) moved between lagoons.
There was significant spatial separation (no overlap) in the percentage of time
sharks spent at VR2’s (based on number of detections) between sharks tagged in the west
and those tagged in the east lagoon for the 2004 (ANOSIM, R = 1.0, p = 0.02) and 2006
(ANOSIM, R = 0.8, p = 0.01) tagging periods (Fig. 23a). Furthermore, when data were
combined for both tagging periods, there were large differences in the duration over
which sharks were detected among sharks tagged in the east and those tagged in the west
145 Table 10. Summary information for sharks tagged with acoustic transmitters in the
Palmyra lagoons. Shark Total Length (TL) is included. Time periods (h) of cyclical
patterns as determined by FFT analysis are also given. ND indicates that there was not
enough data to perform FFT analysis. Sharks codes with * indicate individuals tagged
with V16 transmitters.
Capture Transmitter TL Sex Date Date first Date last Overall Total Spectral location code (cm) deployed detected detected detection detections peaks (h) period (d) Banjos 62 122 F 20 Mar 04 24 Mar 04 25 Mar 07 1097 1211 24,12,8 Banjos 56 105 M 20 Mar 04 23 Mar 04 24 Jul 05 489 1240 24,6 Nursery 51 111 F 22 Mar 04 23 Mar 04 9 Jun 05 444 527 24,8 Nursery 57 112 M 22 Mar 04 25 Mar 04 6 Oct 06 926 4307 No peaks Nursery 58 132 F 22 Mar 04 - - - - - Nursery 63 101 M 22 Mar 04 22 Mar 04 25 May 07 1160 5057 24 Nursery 60 108 M 22 Mar 04 31 Mar 04 14 Jan 06 655 141 24,6 Banjos 5 114 M 26 Feb 05 - - - - - Nursery 29* 123 F 24 Feb 05 25 Feb 05 12 Oct 07 960 150348 24,12 Sixes 53 115 F 21 Mar 04 17 May 04 21 Sep 04 128 52 24,4 Sixes 81 111 F 21 Mar 04 23 Mar 04 20 Apr 04 29 431 24,4 Sixes 61 100 M 21 Mar 04 22 Mar 04 13 May 04 54 1205 No peaks Sixes 85 92 M 21 Mar 04 - - - - Sixes 89 111 M 21 Mar 04 22 Mar 04 8 May 04 49 1858 24 Sixes 50 100 F 21 Mar 04 23 Mar 04 30 Aug 05 526 801 24,8 Sixes 80 110 M 21 Mar 04 22 Mar 04 26 Apr 04 36 1404 24,2.5 Sixes 83 122 F 21 Mar 04 22 Mar 04 25 May 04 65 3191 24,8 Nursery 3226 89 F 12 Oct 06 12 Oct 06 13 Feb 07 125 686 No peaks Nursery 3224 90 F 12 Oct 06 15 Oct 06 31 Jan 07 109 82 24 Nursery 3223 123 F 12 Oct 06 13 Oct 06 13 Jul 07 274 132 26 Nursery 3230 101 F 12 Oct 06 17 Oct 06 8 Mar 07 143 18 ND Nursery 3232 94 F 12 Oct 06 17 Oct 06 26 Oct 06 9 17 ND Nursery 3227 100 F 12 Oct 06 12 Oct 06 17 May 07 218 695 No peaks Nursery 3222 106 M 13 Oct 06 - - - - Nursery 3228 108 M 13 Oct 06 13 Oct 06 2 Dec 06 51 14 ND Nursery 3216 101 F 13 Oct 06 14 Oct 06 21 Feb 07 131 95 No peaks Nursery 3225 88 M 13 Oct 06 - - - - - Nursery 3217 99 M 13 Oct 06 - - - - - Nursery 3231 95 M 13 Oct 06 - - - - - Nursery 3234 120 F 13 Oct 06 - - - - - Nursery 3201 105 M 13 Oct 06 - - - - -
146 Sixes 3205 96 F 14 Oct 06 14 Oct 06 15 Dec 06 63 13 ND Sixes 3204 107 M 14 Oct 06 16 Oct 06 5 Dec 06 51 49 ND Sixes 3203 83 M 14 Oct 06 14 Oct 06 4 Feb 07 114 16 ND Sixes 3212 104 M 14 Oct 06 15 Oct 06 22 Feb 07 131 1301 24,8 Sixes 3211 127 F 14 Oct 06 - - - - - Nursery 3235 112 M 14 Oct 06 14 Oct 06 26 Jul 07 286 497 No peaks Nursery 3214 117 M 14 Oct 06 - - - - - Nursery 3229 112 M 14 Oct 06 - - - - - Nursery 3207 113 M 14 Oct 06 14 Oct 06 13 Oct 07 365 6268 No peaks Nursery 3210 101 M 14 Oct 06 - - - - - Nursery 3208 94 M 16 Oct 06 - - - - - Sixes 3202 86 M 29 May 31 May 07 11 Jul 07 42 168 12 07 Sixes 3221 86 F 29 May 29 May 07 14 Jul 07 47 162 No peaks 07 Sixes 3218 77 M 29 May 29 May 07 9 Oct 07 134 784 24,12 07 Sixes 3* 104 M 29 May 29 May 07 12 Oct 07 137 6872 24,12 07 Banjos 3213 95 M 29 May 30 May 07 28 Jun 07 30 115 24 07 Banjos 4* 130 F 29 May 29 May 07 13 Oct 07 138 3870 24,12,8 07 Banjos 6* 119 M 31 May 31 May 07 22 Aug 07 84 1310 24,12,8 07
147 Figure 22. Detection matrix for individual sharks by VR2’s throughout the Palmyra lagoons. Shark numbers in bold were tagged in the east lagoon, the remainder were tagged in the west lagoon. Receivers Sixes, Downeast and Cookies (in bold) are located in the east lagoon. All remaining receivers were in the west lagoon.
148
Shark # Sixes Downeast Cookies Midchannel Airport Nursery Banjos west Eddies 3 3202 3203 3204 3205 3212 3218 3221 83 80 50 89 61 53
4 6 3207 3213 3216 3223 3224 3226 3227 3228 3230 3232 3235 60 63 62 57 56 51 29
149 Table 11. Percentage detections at individual receivers by blacktip reef sharks. W 04 and W 06 are sharks tagged in the west lagoon during 2004/2005 and 2006/2007 respectively. E 04 and E 06 are sharks tagged in the east lagoon during 2004/2005 and
2006/2007. VR2’s are Sixes (Six), Downeast (DE), Cookies (Cook), Midchannel (Mid),
Jenns (Jens), Airport (Air), Nursery (Nur), east Banjos (Banj. E), west Banjos (Banj. W),
Inner Eddies (Inn. Edd.), Eddies (Edd), Barge (Bar) and the Outer Channel (Out. Chan).
All receivers are located in the west lagoon except for Sixes, Downeast, and Cookies, which are in the east lagoon. Median (Med.) and third quartile (Q3) of % detections are also given.
Six DE Cook Mid Jens Air Nur Banj. Banj. Inn. Edd Bar. Out. E W Edd. Chan.
W 04 Med 0.1 0 0 3.2 - 15.2 1.0 - 33.5 - 0.4 - - Q3 3.6 0.2 0.3 22.6 - 90.1 2.1 - 81.5 - 14.1 - - W 06 Med 0 0 0 0 0 28.5 0.3 14.3 4.2 0 1.1 1.1 0 Q3 0 0 0.7 1.2 3.7 71.6 7.8 25.9 18.9 0.6 7.3 6.9 4.0 E 04 Med 11.4 28.1 32.0 0 - 0 0 - 0 - 0 - - Q3 38.5 56.6 92.9 0 - 0 0 - 0 - 0 - - E 06 Med 74.4 1.1 7.4 0 0 0 0 0 0 0 0 0 0 Q3 98.9 19.4 16.8 0.9 0 0.1 0 0 0 0 0 0 0
150 Figure 23. Non-metric multidimensional scaling ordination of spatial overlap between a)
sharks tagged in the east (black triangles) and the west (grey triangles) lagoon during
2006, b) male (black circles) and female (clear circles) sharks tagged in the west lagoon
in 2006.
151 Stress: 0.07 a
W
E
b Stress: 0.08
F M
152 lagoon. Sharks tagged in the west lagoon were detected over a longer duration (median
280 d, range 9 – 1160 d) than sharks tagged in the east lagoon (median 63 d, range 29 –
526 d, Mann Whitney w = 278, p = 0.008).
There was also evidence of sexual segregation of sharks in the west lagoon during
2006 with significant spatial separation (no overlap) between males and females
(ANOSIM, R = 0.69, p = 0.01). nMDS plots visually demonstrate sexual segregation
(Fig.23b) and are caused by female sharks having a greater proportion of detections at the
airport VR2, while males had a greater percentage of detections at the east or west Banjos
VR2’s (Fig.21). For sharks tagged during 2004 in the west lagoon, the EcoSim null
model revealed that only the two tagged female sharks showed significant overlap with
each other (Pianka overlap = 0.99, p = 0.01, Table 12a). Again, this was due to a high
percentage of detections at the airport receiver for females. There were multiple pair-
wise cases of significant overlap between male and female sharks in the east lagoon, for both 2004 and 2006 (Table 12b, c).
For the 29 sharks with enough data to run FFT analysis, 20 (69 %) showed 24 h
(diel) peaks, and 15 (52 %) showed 6, 8, or 12 h (tidal) peaks (Table 10, Fig. 24). Of the
17 sharks tagged in the west lagoon, 11 (65 %) showed diel peaks and 7 (41 %) had tidal
peaks. Of the 12 sharks from the east lagoon, 9 (75 %) showed diel peaks and 6 (50 %)
had tidal peaks (Table 10, Fig. 24). However, the FFT analysis also revealed that there
were inter-individual differences in the magnitude and consistency of these behaviors
(Fig. 24).
153 Table 12. Null model pair-wise spatial overlap estimates for acoustically tagged blacktip
reef sharks. Transmitter codes correspond to those in table 10. Values in bold represent statistically significant levels of overlap, as determined by the null model. a) Pianka overlap indices for sharks tagged in the west lagoon in 2004. Sharks #51 and #62 are female. b) Pianka overlap indices for sharks tagged in the east lagoon 2004. Sharks #81,
#50, #53 are female. c) Pianka overlap indices for sharks tagged in the east lagoon 2006.
Sharks #3205 and #3221 are female.
a 60 63 57 56 51 62 60 - 0.09 0.02 0.03 0.26 0.24 63 - 0.98 0.95 0.15 0.22 57 - 0.99 0.07 0.15 56 - 0.16 0.23 51 - 0.99 62 - b 81 50 53 89 61 81 - 0.50 0.59 0.96 1 50 - 0.99 0.23 0.50 53 - 0.33 0.59 89 - 0.96 61 - c 3205 3221 3 3202 3203 3204 3212 3218 3205 - 0.78 0.75 0.74 0.69 0.67 0.41 0.74 3221 0.98 0.98 0.88 0.88 0.42 0.98 3 0.99 0.87 0.91 0.24 0.99 3202 0.85 0.90 0.22 1 3203 0.95 0.41 0.85 3204 0.21 0.90 3212 0.22 3218
154 Figure 24. Scatter-plots (a, c, e) and FFT spectral analysis (b, d, f) of shark movements at
Palmyra atoll. a) and b) are for shark 3212 tagged in the east lagoon, c) and d) are for shark 6 tagged in the west lagoon, and e) and f) are for shark 61 tagged in the east lagoon. Note there are no diel or tidal peaks in the spectral analysis for shark 61. Y-axes for spectral analysis are on different scales.
155 a b 00:00:00 25 24 20:00:00 20 8
16:00:00 15 12:00:00
Time 10 08:00:00
5
04:00:00 Spectral density
00:00:00 0 11/1/06 12/1/06 1/1/07 2/1/07 10 20 30 40 Date Time (h)
c d 00:00:00 300 24 20:00:00 250
16:00:00 200
12:00:00 150 Time 08:00:00 100 12
04:00:00 Spectral density 50
00:00:00 0 6/4/07 6/18/07 7/2/07 7/16/07 7/30/07 8/13/07 10 20 30 40 Date Time (h)
e f 00:00:00 20
20:00:00 15 16:00:00
12:00:00 10 Time 08:00:00 5
04:00:00 Spectral density
00:00:00 0 3/21/04 4/4/04 4/18/04 5/2/04 5/16/04 10 20 30 40 Date Time (h) Banjos east Cookies Midchannel Nursery Sixes
156 Stable isotopes and body condition indices
We obtained muscle tissue from 63 sharks, (39 from the west and 24 from the east
lagoon) which ranged in TL from 75 – 130 cm. The GLM showed a significant effect of
location (F = 9.13, d.f. = 62, p = 0.04), TL (F = 11.96, p = 0.01) and an interaction term
between TL and location (F = 8.91, p = 0.04) on δ15N, although there was no effect of sex
(F = 0.53, p = 0.47). We therefore analyzed data separately for east and west lagoon
sharks, using multiple regression analysis. Shark TL did not influence δ15N for west lagoon sharks (F = 0.10, d.f. = 38, p = 0.76) but did for sharks caught in the east lagoon
(F= 19.54, d.f. = 23, r2 = 0.42, p < 0.001, Fig. 25b). There was no effect of sex for either
west (F = 0.03, p = 0.86) or east (F = 2.72, p = 0.11) lagoon sharks. Overall, however,
there was no difference in δ15N between west (14.64 ± 0.52 ‰ (mean ± 1SD)) and east
lagoon sharks (14.54 ± 0.67 ‰, t = 0.65, d.f. = 39, p = 0.52). There was also no
difference in TL between west (102.4 ± 12.6 cm) and east lagoon sharks (98.6 ± 12.1 cm,
t = 1.19, d.f. = 50, p = 0.24).
Similarly, the GLM revealed significant effects of location (F = 4.06, d.f. = 62, p
= 0.049) and an interaction term between location and TL (F = 4.05, p = 0.049) on δ13C, but no effect of sex (F = 0.11, p = 0.74) or TL (F = 2.38, p = 0.13). Multiple regression analysis was used to analyze data separately for east and west sharks. There was no effect of shark TL on δ13C for sharks in the west lagoon (F = 0.19, d.f. = 38, p = 0.67),
but there was a negative relationship for sharks in the east lagoon (F = 7.35, d.f. = 23, r2 =
0.24, p = 0.013, Fig. 25c). Sex had no effect on δ13C for either west (F = 0.71, p = 0.41)
or east (F = 0.69, p = 0.41) lagoon sharks. Again, however, there was no overall
157 Figure 25. Effect of shark total length (TL) on a) body condition index (BC, length/girth)
b) 15N c) 13C heavy stable isotopes. Sharks have been separated based on lagoon where they were caught (West or East). The dashed red line in a) represents the mean BC value for each lagoon.
158 a West East 3.2 3.2 y = -0.0016x + 2.88 y = -0.0082x + 3.45 2 2 3.0 R = 0.01, p=0.39 3.0 R = 0.43, p<0.001
2.8 p=0.39 2.8
2.6 2.6 BC BC
2.4 2.4
2.2 2.2 p=0.0001
2.0 2.0 60 80 100 120 80 90 100 110 120 130 TL (cm) TL (cm)
b 16.5 16.5 y = 0.002x + 14.40 y = 0.036x + 11.00 2 16.0 2 16.0 R = 0.003, p=0.73 R = 0.42, p<0.001 15.5 15.5 p=0.0006
15.0 15.0 δ15N δ15N 14.5 14.5
14.0 14.0
13.5 13.5 p=0.73 13.0 13.0 70 80 90 100 110 120 130 140 70 80 90 100 110 120 130 TL (cm) TL (cm)
c -6 -6 y = 0.006x - 10.72 y = -0.058x - 4.27 2 p=0.76 -7 2 -7 R = 0.003, p=0.76 R = 0.24, p=0.016 p=0.016 -8 -8 13 13 δ C -9 δ C -9
-10 -10
-11 -11
-12 -12
-13 -13 70 80 90 100 110 120 130 140 70 80 90 100 110 120 130 TL (cm) TL (cm)
159 difference in δ13C between west (-10.10 ± 1.52 ‰) and east lagoon sharks (-9.95 ± 1.44
‰, t = -0.39, d.f. = 50, p = 0.70).
Body condition indices (BC) were obtained from 103 sharks, of which 74 were from the west and 29 from the east lagoon. There were no significant interaction effects
(F= 2.68, d.f. = 102, p = 0.11), so they were removed from the model. The GLM
revealed a significant effect of location (F = 8.31, p = 0.005) on BC, but no effect of sex
(F = 1.94, p = 0.17) or TL (F = 2.59, p = 0.11). When analyzed separately for each
lagoon, there was no effect of sex (F = 1.14, p = 0.29) or TL (F = 0.33, p = 0.57) on BC
for sharks in the west lagoon. For sharks in the east lagoon, sex had no effect (F = 0.13,
p = 0.72) but TL had a negative influence on BC (F = 13.91, r2 = 0.43, p = 0.001, Fig.
25a). BC indices were higher for sharks in the west lagoon (2.71 ± 0.21) than those in
the east lagoon (2.60 ± 0.15, t = - 3.0, d.f. = 72, p = 0.004).
DISCUSSION
Both abiotic and biotic conditions can dictate the quality of a habitat for a
particular animal. Generally, there is a trade off between a habitat providing high
foraging success or safety from predation, although abiotic conditions such as water
temperature and dissolved oxygen can also provide physiological constraints on an
animal (e.g. Lima and Bednekoff 1999, Moore et al., 1998). Blacktip reef sharks at
Palmyra appear to differ in their residence time based on which lagoon they reside in, and
sharks in different lagoons also have overall differing levels of foraging success.
The frequency of movements between the east and west lagoon appear to be low for blacktip reef sharks at Palmyra Atoll. Although 47 % of sharks showed movements
160 between lagoons, those sharks that did move between lagoons (with the exception of two
sharks not used in the analysis) only did so for short periods with most movements
consisting of brief excursions. Why such infrequent and brief migration rates occur
between lagoons are unclear, although physical characteristics of the lagoons may play a role. Access between the lagoons either requires swimming through a shallow channel with strong tidal currents, or traversing several kilometers of shallow sand-flats around the islands surrounding the lagoons (see Fig.21). Although blacktips are certainly capable of swimming in very shallow water, the physical characteristics of the lagoons may reduce the chance that sharks from one lagoon visit the other.
The majority of sharks in both lagoons showed diel and tidal movements, although there were inter-individual differences in the magnitude and consistency of these behaviors. Such differences could simply be a consequence of the placement of
VR2 receivers (e.g. a VR2 may be situated in the center of a sharks home range), but some sharks showed no indication of diel behavior. Intra-specific competition has been shown to drive disruptive selection of phenotypes in a population (Pfenning et al., 2007), and this may also be true of foraging tactics and behaviors. Individual specifications in dietary selection and foraging behavior are likely to evolve in apex predators when intra- specific competition is high while inter-specific competition is low (Estes et al., 2003).
Qualitatively, Palmyra should meet these requirements, as blacktips are the major apex predators in the lagoons, and their high numbers in the lagoons may promote high levels of intra-specific competition (Papastamatiou Chapter 5). Furthermore, blacktip reef sharks at Palmyra are smaller than those in other locations and this may be due to food- limited growth resulting from intra-specific competition (Papastamatiou Chapter 5). The
161 diel and tidal movements in adult blacktip reef sharks are most likely related to foraging and may increase the likelihood of prey capture (see Papastamatiou Chapter 4). Although sharks in both lagoons showed diel and tidal peaks, some sharks in the east lagoon also showed cyclical patterns of detections with periods of 2.5 and 4 h. It is unclear why these patterns were not observed for sharks in the west lagoon, but may be related to small differences in tidal flow characteristics between the two lagoons.
We also revealed that some degree of sex segregation occurs in the west lagoon, with male and female sharks showing preferential use of different areas. Sex segregation is common in shark populations and most likely enables females to avoid energetically costly mating behavior during periods outside of the mating season (see Sims 2003).
This further implies that blacktip reef sharks move to specific locations at Palmyra Atoll to mate, which may explain the seasonal movements between lagoons observed in a couple of sharks (Papastamatiou Chapter 4). Sexual segregation was also suggested from fishing at Palmyra, with sex ratios skewed towards females at the Channel ledges
(Papastamatiou Chapter 5). However, not all the female sharks acoustically tagged were sexually mature (all males were), so the causative factors behind segregation are still unclear. Nonetheless, the present study highlights that sex segregation of shark populations can occur at scales of hundreds of meters, presumably a necessity due to the small size of Palmyra Atoll.
Sharks tagged in the west lagoon were detected for longer durations than sharks tagged in the east lagoon. Although some sharks were detected over several months in the east lagoon, some of those tagged in the west lagoon were detected over several years. When sharks are not detected they must have moved on to sand-flats, or more
162 likely the outer fore-reefs, suggesting that conditions in the west lagoon are more
desirable than in the east lagoon. In addition, east lagoon sharks were not subsequently
detected either in the east or west lagoons, which means they must perform a complete
home range shift. There were no instances of east lagoon sharks shifting their home
range into the west lagoon. There was variability in residence times of west lagoon
sharks, with some individuals detected for only short periods of time. Six sharks were
observed to gradually move out of the west lagoon via the main channel over a period of a couple of months. Presumably, some form of density dependent regulation will dictate that the west lagoon has a carrying capacity and not all individuals will be able to reside there indefinitely. These movements could have also been related to reproduction or mating but sharks that were detected leaving the lagoon, were a mixture of mature and immature, males and females.
Stable isotope and body condition indices (BC) show that foraging success is
greater for sharks in the west lagoon and that the relationship between trophic position and shark length differs between lagoons. Specifically, shark total length is not a predictor of 15N, 13C, or BC in the west lagoon. In the east lagoon, tissue levels of 15N increase with shark size, which may be an indicator of increased trophic position as they get larger. On the other hand, BC decreases and 13C becomes more depleted as sharks in
the east lagoon increase in size. Some authors suggest that fasting animals increase the
relative concentration of 15N in their tissues (Martinez del Rio and Wolfe, 2005). As the
rate of nitrogen excretion exceeds the rate of nitrogen input in an animal, the relative 15N concentration should also increase relative to pre-fast levels. This is thought to occur
because nitrogen loss in a fasting animal will be primarily urinary, which is enriched with
163 the lighter isotope, N14 (Martinez del Rio and Wolfe, 2005). In the east lagoon, the
increase in 15N with shark size, and the concurrent decrease in BC could be an indicator that larger sharks in the east lagoon are in nitrogen deficit and for some reason have a harder time obtaining adequate resources than smaller individuals. However, presently no calibrations have been performed to determine if fasting sharks do become enriched in
15N.
The main implication of decreasing 13C levels is a shift in the carbon source of
prey from a coastal to a more pelagic origin, due to differences in 13C between benthic
and planktonic phytoplankton (France 1995, Miller et al., 2008). Palmyra reef fish
caught in the lagoons are more enriched in 13C than individuals caught on the outer reefs
(S.Sandin unpublished data). Furthermore, there will be substantial input of terrestrial carbon into the lagoon ecosystem, which will essentially be absent in the pelagic ecosystem. The depletion of 13C in larger sharks in the east lagoon could occur because
the larger sharks forage more on the fore-reefs, or also possibly because they include a
greater proportion of seabirds in their diet. Seabirds generally forage off-shore, and
adults therefore provide pelagic prey to their chicks, giving them relatively depleted 13C signatures relative to other coastal animals, which is also apparent in Palmyra seabirds
(Stapp 2002, H. Young, unpublished data). Examination of a small number of blacktip stomach’s at Palmyra showed that sharks do consume seabirds, which are present in large numbers at the atoll, although it is unknown how much of this is scavenging versus active predation (Papastamatiou Chapter 5). The wide range in 13C values for all blacktip reef
sharks (range -12.6 to -7.0), may also suggest large individual differences in diet. Again,
dietary diversity and the evolution of individual dietary specialization is thought to
164 evolve in apex predator populations with high levels of intra-specific competition, as this alleviates interference and promotes resource partitioning (Estes et al., 2003).
Several causative factors could explain the differences in foraging success between lagoons. Firstly, environmental conditions differ slightly between the lagoons, with the predominantly NE swell passing into the east lagoon first. These cause the generally lower water temperatures in the east lagoon and may also affect other oceanographic conditions (Friedlander unpublished data). Secondly, the shorter residence times implies that sharks in the east lagoon may have to invest more energy into movements and foraging over a larger area, which would explain the lower body condition indices. These sharks may also have to forage on the outer reefs, where resources may be harder to obtain and foraging success reduced under the fore-reef conditions (the predator assemblage on the fore-reefs shifts from blacktip reef sharks to grey reef sharks, Carcharhinus amblyrhincus). Although, fish population transects of core ledges suggests that potential prey biomass is similar between the lagoons (e.g. for all fish < 50 cm SL, biomass at Banjos is 72.4 ± 18.7 g/m2, at Sixes 84.9 ± 27.1 g/m2, t =
-0.89, p = 0.41, D.J. McCauley unpublished data), foraging opportunities may still be lower in the east lagoon, due to the smaller size of the lagoon. There do not appear to be differences in the abundance of blacktips between the lagoons (based on catch per unit effort, Papastamatiou Chapter 5), which would imply that levels of intra-specific competition in the east lagoon are also higher. The lack of a relationship between shark size and 15N, 13C, and BC could also suggest that smaller sharks in the west lagoon have greater foraging success as resources are less limiting and therefore they are better able to compete with larger sharks. Finally, the large channel connecting the outer reefs to the
165 west lagoon, may simply allow for easier access of a wide variety of habitats, which may
also lead to the lack of a relationship between carbon isotope signatures and shark length.
One of the major theoretical models predicting predator distribution is the Ideal
Free Distribution (IFD), where predator density in a patch is proportional to habitat quality (Fretwell and Lucas 1970). The IFD further predicts that the “free” distribution of predators across habitats of varying quality will ensure that all animals obtain equal prey consumption rates. Blacktip reef sharks at Palmyra do not appear to conform to an
IFD as abundance in west and east habitats are similar (despite differences in size), while foraging success and presumably intake rates are different (Papastamatiou Chapter 5). A more realistic model, incorporating predator migration rates, predicts that low migration rates of predators between patches, would not lead to an IFD and would also promote predator-prey stability (Bernstein et al., 1999). The low migration rates of sharks between east and west lagoons and the apparent deviation from IFD conforms to this prediction. However, it is unclear how sharks respond to prey patches at a smaller scale.
Active tracking of blacktips in the west lagoon suggests that sharks respond differently to certain sand-flat ledges, and that they utilize small patches at scales of 30 – 200 m
(Papastamatiou Chapter 4). Even though most sharks were tagged by the Nursery sand- flats, very few detections occurred at the Nursery receiver, further indicating that the
Nursery ledge is “poor” habitat for blacktip reef sharks (Papastamatiou Chapter 4). Both active and passive tracking show that sharks demonstrate strong site fidelity to the Banjos ledge, even though the lowest Catch-Per-Unit-Effort (CPUE) in all the Palmyra lagoons were obtained at Banjos (Papastamatiou Chapter 4,5). Therefore, a small number of sharks show strong site fidelity to Banjos ledge, after which it is apparently more
166 advantageous for additional sharks to forage in other locations or over a larger area. The
Banjos area has a smaller ledge than the others in the west lagoon, so this result may conform to the predictions of the IFD. However, some caution needs to be shown when inferring abundance of sharks from CPUE data, as CPUE can also be dependent on water current regimes and dispersal of bait (see Papastamatiou Chapter 5).
This study highlights the effect habitat can have on movements and foraging success of a population of marine apex predators. Designing effective MPA’s for elasmobranch populations will require predicting how animals will move and behave in a variety of habitats, and the development of mechanistic home range models (e.g.
Moorcroft and Barnett 2008). Understanding why predators do not conform to basic theoretical predictions is an important step in the development of these more general laws of movement, in particular in locations where predator populations are under strong levels of density-dependent regulation. Future studies should determine if similar responses to habitat are seen in sharks in areas with lower population densities, or if these results are partially driven by interference from intra-specific competition.
167 Chapter VII
Conclusion
Optimal foraging theory provides a framework with which we can try to explain dietary choices by an animal as well as foraging behaviors and decisions. The research conducted for my dissertation also suggests that it may be important to also include studies of digestive physiology when developing optimal foraging models, particularly for large predators. The primary predictions I make based on measurements of gastric pH, motility and temperature in free-swimming blacktip reef sharks are.
1) Blacktips in the field forage daily
2) Optimal meal size should approximate 1 % body weight
3) Sharks should preferentially forage during periods of low water temperature. For
the environment occupied by these sharks, such periods would occur during the
early morning hours.
Although I was not able to directly prove these hypotheses, I was able to provide evidence to support some of these predictions. Active tracking of shark’s shows reduced rates of movement at Palmyra Atoll during the nocturnal flood tide, which corresponds with a precipitous decrease in water temperature. These reduced rates of movement could indicate area restricted searching, but this can not be verified by active tracking alone. The constant patrolling by sharks and movements over ledges and sand-flats also suggests that these sharks have an active life style, with obligate RAM swimming, which could also suggest a relatively high rate of food consumption.
168 However, verifying these hypotheses will require the development of sensors
which can directly quantify feeding and digestive processes. I also successfully
developed such a tool, the acoustic pH transmitter, and showed that it can be used to
quantify feeding events in blacktip reef sharks, although I was not able to obtain field
data.
In addition, my study also showed the importance that habitat can play in both
shark movements and foraging success. Both active and passive tracking indicated that
certain ledges provide desirable conditions for sharks, as they showed high site fidelity to
these areas, even over long time periods. Other ledges, appear to act as only transient
habitats, as sharks did not show high degrees of site fidelity to these locations.
Furthermore, habitat on a slightly larger scale (east versus west lagoons) played a strong role in both the foraging success of the sharks in the lagoon and the trophic relationship
of those animals. The main response by the sharks is that their residence times also
appear to vary based on which lagoon they reside in. Sharks reside longer in the west
lagoon, where the appear to benefit by increased levels of foraging success.
The small size of blacktip reef sharks at Palmyra, and the change in foraging
success over small spatial scales, may indicate that intra-specific competition is playing a
strong regulatory role for this population of apex predators. Understanding the role of such competition as well as the effect of habitat on movements and foraging success will be crucial for the eventual development of effective MPA’s, and habitat and population density dependent mechanistic home range models.
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