NEVER JUDGE A BOOK BY ITS COVER · Bradshaw, C.J.A. (2011). Decoding fingerprints: elemental...

NEVER JUDGE A BOOK BY ITS

COVER –

Comparative life history traits of two morphologically

similar carcharhinid sharks in northern Australia.

By Bree J Tillett

September 2011

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NEVER JUDGE A BOOK BY ITS COVER – comparative life history traits of two

morphologically similar carcharhinid sharks in northern Australia.

By

Bree Tillett: PhD Candidate

BSc (Hons) Centre for Marine Studies, University of Queensland

Faculty of Engineering, Health, Science and the Environment

Charles Darwin University / Australian Institute of Marine Science

Ellengowan Drive Brinkin, Darwin, Northern Territory, AUSTRALIA

A thesis submitted in fulfilment of the requirements of the degree

Doctor of Philosophy

Charles Darwin University

SEPTEMBER 2011

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Cover picture from (Last & Stevens 2009)

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Sunrise during shark surveys at East Alligator River, Kakadu National Park, Northern

Territory

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Declaration of Originality

I hereby declare that the work herein, now submitted as a thesis for the degree of Doctor of

Philosophy of the Charles Darwin University, is the result of my own investigations, and all

references to ideas and work of other researchers have been specifically acknowledged. I

hereby certify that the work embodied in this thesis has not already been accepted in

substance for any degree, and is not being currently submitted in candidature for any other

degree.

29/8/2011

Bree Tillett Date:

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Dedication:

To my parents, friends and Darwin sunsets for continued encouragement and clarity through

challenging moments.

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Declaration of publication and co-authorship:

Publications produced as part of this thesis:

Publication as section in: Dudgeon, C.L. , Blower, D.C., Broderick, D., Giles, J.L., Holmes,

B.J., Kashiwagi, T., Krück, N.C., Morgan, J.A.T., Tillett, B.J. and Ovenden, J.R. (2012). A

review of the application of molecular genetics for fisheries management and conservation of

sharks and rays. Journal of Fish Biology. 80:1789-1843. DOI:10.1111/j.1095-

8649.2012.03265.

Tillett, B.J., Meekan, M.G., Field, I.C., Hua, Q. and Bradshaw, C.J.A. (2011). Similar life

history traits in bull (Carcharhinus leucas) and pigeye (C. amboinensis) sharks. Marine and

Freshwater Research. 62: 850-860. DOI:10.1071/MF10271.

Tillett, B.J., Meekan, M.G., Parry, D., Munksgaard, N., Field, I.C., Thorburn, D. and

Bradshaw, C.J.A. (2011). Decoding fingerprints: elemental composition of vertebrae

correlate to age-related habitat use in two morphologically similar sharks. Marine Ecology

Progress Series. 434: 133-142. DOI:10.3354/meps09222.

Tillett, B.J., Meekan, M.G., Broderick, D., Field, I.C., Cliff, G. and Ovenden, J.R. (2012).

Pleistocene isolation, secondary introgression and restricted contemporary gene flow in the

pigeye shark, Carcharhinus amboinensis across northern Australia. Conservation Genetics.

13: 99-115. DOI: 10.1007/s10592-011-0268-z.

Tillett, B.J., Meekan, M.G., Field, I.C., Thorburn, D and Ovenden, J.R. (2012). Evidence for

reproductive philopatry in the bull shark, Carcharhinus leucas in northern Australia. Journal

of Fish Biology. In press. DOI: 10.1111/j.1095-8649.2012.03228.x

The following people and institutions contributed to the publication of the work undertaken

as part of this thesis:

Mark G. Meekan (Australian Institute of Marine Science) assisted with general

supervision.

Iain C. Field (Charles Darwin University, now at Macquarie University) assisted with

field work and general supervision.

Jennifer R. Ovenden (Queensland Department of Employment, Economic

Development and Innovation) assisted with the development of genetic skills and

understanding genetic modes of inheritance

Damien Broderick (Queensland Department of Employment, Economic Development

and Innovation) assisted with genetic protocols and technical development.

David Parry (Australian Institute of Marine Science) assisted with methods of

chemical analyses.

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Neils Munksgaard (Charles Darwin University) assisted with the operation of the LA-

ICP-MS.

Dean Thorburn (Indo-Pacific Environmental PTY LTD) provided samples.

Geremy Cliff (KwaZulu-Natal Sharks Board) provided samples.

Quan Hua (Australian Nuclear Science and Technology Organisation) assisted with

bomb-radiocarbon analysis

Corey J.A. Bradshaw (Adelaide University & South Australian Research and

Development Institute) assisted with project development and research outline.

We the undersigned agree that with the above stated “proportion of work undertaken” for

each of the above published (or submitted) peer-reviewed manuscripts contributing to this

thesis:

Michael Douglas Andrew Campbell

(Principal supervisor) (Head of School)

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Acknowledgements

I thank Tropical Rivers and Coastal Knowledge (TRaCK) research hub at Charles Darwin

University and the Australian Institute of Marine Science for providing financial and logistic

support from which I was able to develop my research ideas.

I thank my supervisors, Mark Meekan, Iain Field, Jennifer Ovenden, David Parry and Corey

Bradshaw for their guidance and access to leading edge technologies such as Laser

Ablation – Inductively Coupled Plasma - Mass Spectrometry (LA-ICP-MS) and bomb radio-

carbon dating; and allowing me to develop my own research direction.

I thank all the external support that I received during my studies. This unexpected in-kind

support helped me look outside the box and appreciate different interests in science. In

particular I thank Kakadu National Park for funding and providing all the necessary facilities

for shark surveys in such unforgiving environments. Northern Territory Fisheries for the use

of their laboratory facilities and boats without which, dissecting medium – large size sharks

would have been far less enjoyable. I also thank the commercial shark fishers themselves for

their continual supply of sharks at no cost and support for sustainable fisheries. Western

Australian and Queensland Fisheries Departments also provided both tissue and vertebral

samples for analysis broadening the range of my research; and monetary support from

Queensland Department of Employment, Economic Development and Innovation was

invaluable. Other researchers such as Dean Thorburn (freshwater bull sharks) and Geremy

Cliff (South African pigeye shark samples) also provided tissue samples which this research

did not have the funds to obtain otherwise. Collaborations with the Fishing and Fisheries

Research Centre at James Cook University also provided invaluable tissue and vertebral

samples from eastern Australia and an external support network to further develop techniques.

I also thank Simon Bloomberg (University of Queensland) for his thorough statistical

discussions simplifying complex statistics and Rcran.

I also acknowledge my friends who kept me sane and distracted as things became more

challenging and my family for their continued reassurance and optimism.

Lastly I thank Pete for his relentless support as research assistant; never shying away from

weekend fishing trips, all in the name of science.

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TABLE OF CONTENTS

List of Figures……………………………………………………………………………...xiv

List of Tables……………………………………………………………………………....xix

Thesis Abstract……………………………………………………………………………xxiii

Chapter 1 - Thesis Introduction

Life histories of target species…………………………………………………………2

Thesis aims and predictions…………………………………………………………...5

Chapter 2 - The application of genetic tools to investigate reproductive

philopatry in sharks and rays.

What is philopatry……………………………………………………………………..7

Identification of philopatry in sharks…………………………………………………9

Evolution of philopatry ……………………………………………………………….14

Conclusions…………………………………………………………………………..16

Chapter 3 - Similar life history traits in bull (Carcharhinus leucas) and

pigeye (C. amboinensis) sharks

Introduction …………………..……………………………………………………...18

Materials & Methods………………………………………………...………………21

Vertebral collection ………………………………………………………….21

Vertebral analysis ……………………………………………………………22

Validation and verification …………………………………………………..24

Radio-carbon analysis ……………………………………………….25

Edge analysis ………………………………………………………..28

Growth models ………………………………………………………………28

Results………………………………………………………………………………..30

Age and growth estimates …………………………………………………...30

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Verification ………………………………………………………………….35

Edge analysis ………………………………………………………..35

Radio-carbon analysis ……………………………………………….35

Growth parameter estimates …………………………………………………36

Discussion …………………………………………………………………………...41

Age and growth parameter estimates of bull and pigeye sharks ………….…41

Verification of annual band deposition in the pigeye shark …………………42

Influence of birth-size variability on parameter estimates …………………..44

Accuracy and precision of parameter estimates………………………….…..44

Acknowledgements……………………………………………………………….......46

Chapter 4 - Decoding fingerprints – elemental composition of vertebrae correlate

to known age-related habitat use in two morphologically similar sharks

Introduction ………………………………………………………………………….47

Materials & Methods ………………………………………………………………..50

Vertebrae collection………………………………………………………….50

Preparation of vertebrae ……………………………………………………..52

Ageing ……………………………………………………………………….53

Preliminary study ……………………………………………………………54

Laser ablation-inductively coupled plasma- mass spectrometry……………..55

Analysis ……………………………………………………………………...57

Results ………………………………………………………………………………..60

Discussion ………………………………………………………………….………..68

Acknowledgements…………………………………………………………………...71

Chapter 5 - Pleistocene isolation, secondary introgression and restricted

contemporary gene flow in the pigeye shark, Carcharhinus amboinensis

across northern Australia.

Introduction…………………………………………………………………………..72

Materials & Methods………………………………………………………………...74

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Sample collection and preservation …………………………………………74

Genomic DNA extraction ……………………………………………………77

Amplification and sequencing – mitochondrial DNA ……………………….77

Amplification and sequencing – nuclear DNA (RAG 1) ……………………79

Amplification and genotyping – microsatellites………………………...…...79

Restricted gene-flow due to Pleistocene sea-level changes …………………81

Current day sympatric cryptic species ……………………………………….85

Isolation by Distance ………………………………………………………...85

Population genetic structure – nuclear marker (microsatellites) …………….86

Results……………………………………………………………………………..…86

Restricted gene-flow due to Pleistocene sea-level changes …………………86

Current day sympatric cryptic species ………………………………..……100

Isolation by Distance …………………………………………………...…..100

Population genetic structure – nuclear marker (microsatellites) ………...…101

Discussion …………………………………………………………………………..102

Acknowledgements …………………………………………………………………105

Chapter 6 - Evidence for reproductive philopatry in the bull shark,

Carcharhinus leucas in northern Australia.

Introduction ………………………………………………………………………...106

Materials & Methods……………………………………………………………….108

Sample collection and preservation…………………………………………108

Genomic DNA extraction …………………………………………………..111

Amplification and sequencing – mitochondrial DNA ……………………...111

Amplification and genotyping – microsatellites …………………………...113

Population structure and philopatry ………………………………………..114

Influence of geographic distance and long-shore barriers to movement on

population structure …………………………………………………….......117

Influence of Pleistocene sea-level changes on population structure ……….119

Estimate of long-term effective population size ……………………………119

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Results………………………………………………………………………………120

Population structure and philopatry ………………………………………..120

Influence of geographic distance and long-shore barriers to movement on

population structure…………………………………………………………129

Influence of Pleistocene sea-level changes on population structure ……….133

Estimate of effective population size……………………………………….133

Discussion .................................................................................................................134

Acknowledgements………………………………………………………………….137

Chapter 7 - Thesis conclusions……………………………………………………………138

References …………………………………………………………………………………144

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LIST OF FIGURES

FIG. 3.1. Capture locations for the pigeye (Carcharhinus amboinensis; n = 199) and bull shark

(Carcharhinus leucas; n = 94) across northern Australia……………………………………………..20

FIG. 3.2. Sagittal section of thoracic vertebrae showing growth increments and terminology.

Bull shark (Carcharhinus leucas; 10 years old)………………………………………………………24

FIG. 3.3. Oceanic circulation patterns in northern Australia and adjacent seas indicating directional

flow of major currents (adapted from Guilderson et al. 2009 and Hua et al. 2005). Dark arrows

indicate direction of flow during the southwest monsoon, light arrows indicate flow during the

northeast monsoon and the dashed arrows indicate year-round currents. ITF – Indonesian Through

Flow. Numbers represent local/regional ΔR values for northern Australia, Lombok and Mentawai. *

indicate capture locations of

samples………………………………………………………………..……………………………….27

FIG. 3.4a. Frequency distribution of total length (mm) size classes of pigeye shark (Carcharhinus

amboinensis; n = 199 (female n = 89, male n = 110); size class range from 600 – 2599 mm………..30

FIG. 3.4b. Frequency distribution of total length (mm) size classes of bull shark (Carcharhinus

leucas; n = 94 (female n = 47, male n = 47); size class range from 600 to > 3000 mm)…………...31

FIG. 3.5. Within principle reader variability represented by average percent error (APE as %) ± 95%

confidence interval for each consecutive number of growth increments. Sample sizes are given for

each corresponding band class. a) Pigeye shark (Carcharhinus amboinensis); b) Bull shark

(Carcharhinus

leucas). ……………………………………………………………………………….…………..…..33

FIG. 3.6. Comparison between mode principle and mean reader two-sigma (± 95 % confidence

interval) age estimates (years). Sample sizes are given for each corresponding age class. a) Pigeye

shark (Carcharhinus amboinensis); b) Bull shark (Carcharhinus leucas)…….……………………..….34

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FIG. 3.7. Frequency of final growth increment characteristics (translucent or opaque) per month for

the pigeye shark (Carcharhinus amboinensis). n = 99. ………………………………………..…….35

FIG. 3.8. Comparison of Δ14C values in birth bands of seven pigeye sharks, Carcharhinus amboinensis,

collected from the Timor Sea with those of the reference oceanic 14

C bomb curves from Mentawai (solid

black line) and Lombok (solid grey line), shown in 1-year running mean values. Dashed lines represent 2σ

uncertainties. The birth band samples are plotted against their estimated ages based on band counting.

Vertical and horizontal error bars, shown in 1σ, represent 14

C analytical and band counting uncertainties,

respectively..………………………………………………………………………….……………….36

FIG. 3.9. Growth models fitted to total length (mm) - at-age (years) data. Solid line: von Bertalanffy

Growth Function (VBGF), dashes: two parameter von Bertalanffy growth function (2VBGF), dotted:

gompertz growth function (GOMPERTZ), dot-dash: two parameter gompertz growth function

(2GOMPERTZ). Growth functions do not incorporate variation in birth size. a) Pigeye shark

(Carcharhinus amboinensis); b) Bull shark (Carcharhinus leucas)…………………………………..40

FIG. 4.1. Capture locations for the pigeye Carcharhinus amboinensis (total n = 88) (39 adults,

49 juveniles) and bull Carcharhinus leucas (total n = 92) (18 adults and 74 juveniles) sharks across

northern Australia..……………………………………………………………………………………51

FIG. 4. 2. Sagittal section of bull shark vertebrae (aged 10 years) displaying annual growth

increments, and descriptions of vertebral attributes. ……………………….…………………………54

FIG. 4.3. Scanning electron microscopy of sagitally sectioned ablated shark vertebrae. Boxes

represent areas in the corpus calcareum which elemental compostion were quantified and compared

(1 mm x 1 mm). a) sample PE168 (Carcharhinus amboinensis) b) sample PE169 (Carcharhinus

amboinesis) c) sample B201 (Carcharhinus leucas) c) sample B229 (Carcharhinus leucas….……..57

FIG. 4.4. Similarity Percentages (SIMPER) – element contributions to bull shark nursery signatures.

Total n = 74, (Fitzroy River n = 16, Daly River n = 15, Liverpool River n = 7, East Alligator River

n = 14, Roper River n = 11, Towns River n = 11)..………………………….………………………..61

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FIG. 4.5. Typical Sr:Ba ratios with age (years) of bull sharks; total n = 18. Dots indicate potential

returns to less saline waters. a) Female patterns; n = 14. b) Male patterns; n = 2..…………………...63

FIG. 4.6. Similarity Percentages (SIMPER) – element contributions to pigeye shark nursery

signatures. Total n = 49, (WA n = 16, NT n = 27, N QLD n = 6)..………………………………..….65

FIG. 4.7. Typical Sr:Ba net cps ratios (counts per second) with age (years) of pigeye sharks; total

n = 39. a) Female patterns, n = 18; b) Male patterns, n = 21..………………………………………...66

FIG. 4.8. Typical element:Ca net cps ratios (counts per second) with age (years) of pigeye sharks;

total n = 39. I) Lithium II) Magnesium III) Manganese IV) Zinc. a) Female patterns, n = 18; b) Male

patterns, n = 21. ……………………………………………………………………………………...67

FIG. 5.1. Pigeye shark (Carcharhinus amboinensis; total n = 324) capture locations. * Indicates

capture locations. Circles indicate Australian population groupings: 1 = Western Australia (n = 41), 2

= Northern Territory (n = 205), 3 = Queensland (n = 54). Inset shows other Indian Ocean populations

sampled; 4 = South Africa (n = 16); 5 = Arabian Sea (n = 8); PNG = Papua New Guinea (no

samples)..……………………………………………………………...………………………………75

FIG. 5.2. Frequency distribution of fork length (mm) size classes for pigeye shark (Carcharhinus

amboinensis; n = 300; female n = 141, male n = 59)..….……………………………………………76

FIG. 5.3. Inferred phylogeny of mitochondrial NADH dehydrogenase subunit 4 (ND4) gene

reconstructed using 95 % statistical parsimony network. Inset Bayesian Inference; rooted with

Carcharhinus amblyrhyncoides and C. leucas, nodal support given as Bayesian posterior probabilities

(n = 324)...…………………………………………………………………………………………….96

FIG. 5.4. Inferred phylogeny of mitochondrial control region gene reconstructed using 95 %

statistical parsimony network. Inset Bayesian Inference; rooted with Carcharhinus amblyrhyncoides

and C. leucas, nodal support given as Bayesian posterior probabilities (n = 324)..……..…………..97

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FIG. 5.5. Inferred phylogeny of concatenated mitochondrial NADH dehydrogenase subunit 4 (ND4;

823 bases) and control region (831 bases) reconstructed using 95 % statistical parsimony network.

Haplotype numbers from Table 1 are given next to each pie chart. The size of the pie chart represents

the frequency of the haplotype. Inset Bayesian Inference; rooted with Carcharhinus amblyrhyncoides

and C. leucas, nodal support given as Bayesian posterior probabilities (n = 324)..…………………..98

FIG. 5.6. Frequency of each clade identified in 95 % statistical parsimony network by location for

Carcharhinus amboinensis for each gene region individually, NADH dehydrogenase subunit 4 (ND4;

823 bases) and control region (831 bases); and concatenated. Total n = 324. Dashed circles represent

population structure tested…………………………………………………………………………….99

FIG. 5.7. Reduced major regression analysis between pairwise geographic distances by sea (km) and

pairwise genetic distances (mtDNA PHIST) between un-pooled captured locations (total n = 12).

Regression y = 1.35x -5.487; R2 = 0.059; p < 0.03…………………………………………………..100

FIG. 6.1. Bull shark (Carcharhinus leucas; n = 169) capture locations…………………………….110

FIG. 6.2. Correlation between pairwise geographic distances by sea (km) and pairwise genetic

distances (mtDNA FST) of un-pooled captured locations (total n = 13)……………………………..129

FIG 6.3. Inferred phylogenies calculated using Bayesian Inference. Nodal support is given as

Bayesian probabilities (n = 169). a) NADH dehydrogenase subunit 4 (ND4); b) control region;

c) concatenated ND4 and control region genes……………………………………………………..130

FIG. 6.4. Statistical parsimony network of concatenated NADH dehydrogenase subunit 4 (ND4) and

control region genes (n = 169). The size of each circle represents the relative frequency of each

haplotype and the colour composition depicts the capture locations which haplotypes were identified.

Each circle represents one mutational event; small colourless checks indicate unidentified

haplotypes…………………………………………………………………………………………....131

FIG 6.5. Statistical parsimony network of concatenated NADH dehydrogenase subunit 4 (ND4) and

control region genes (n = 169). The size of each circle represents the relative frequency of each

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haplotype and the colour composition depicts the capture locations which haplotypes were identified.

Each circle represents one mutational event; small colourless checks indicate unidentified

haplotypes……………………………………………………………………………………..……..132

FIG. 6.6. The observed pairwise difference and the expected mismatch distribution (represented by

the line) under the sudden expansion model of concatenated NADH dehydrogenase subunit 4 (ND4)

and control region haplotypes for all locations pooled as one population for Carcharhinus

leucas (n = 169)…………………………….………………………………………………………..133

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LIST OF TABLES

TABLE 3.1. Sampling details for fishery independent survey’s (species combined). Location, sample

size (sex ratio), species, average total length (TL) ± standard deviation (mm), capture date

(months / years)……………………………………………………………………………………….22

TABLE 3.2. ΔR values in northern Australia were used for the calculation of average regional ΔR

value of 63 ± 60 yr for this region shown in Fig. 3.3. All data were taken from the on-line ΔR

database of Reimer & Reimer (2001)…………………………………………………..……….….. 26

TABLE 3.3. Information-theoretic growth model ranking for pigeye, Carcharhinus amboinensis

(males n = 110 and females n = 89) and bull, C leucas (sexes combined n = 94) sharks. AIC (Akaike’s

information criterion); Δ AICc (differences between model AIC values); ωi (AICc weights)………. 37

TABLE 3.4. Age and growth parameter estimates for a) pigeye, Carcharhinus amboinensis (males n

= 110; females n = 89) and b) bull, C. leucas (sexes combined n = 94) sharks. L∞ , theoretical

asymptotic length; k, growth rate; t0, age when the individual would be zero length……………… 39

TABLE 4.1. Sampling details for fishery independent survey’s (species combined). Location, sample

size (sex ratio), species, average total length (TL) ± standard deviation (mm), date (months /

years)………………………………………………………………………………………………..... 52

TABLE 4.2 Operating parameters for LA- ICP-MS (Agilent 7500ce)………………………………55

TABLE 4.3. Pairwise ANOSIM correlations among Carcharhinus leucas nurseries indicating

whether differences are due to distinct environmental signatures or random factors *……………… 62

TABLE 4.4. Average squared distance (standard deviation) between adult birth bands and return

periods with nurseries defined by juvenile sharks………………………………………………...…. 64

TABLE 4.5. Pairwise ANOSIM correlations among Carcharhinus amboinensis nurseries indicating

whether differences are due to distinct environmental signatures or random variation. WA = Western

Australia, NT = Northern Territory, N QLD = North Queensland……………………………..….…64

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TABLE 5.1. PCR reaction conditions for sequenced DNA. a) PCR reaction mixtures, b) PCR

thermocycling conditions…………………………………………………………………………...78

Table 5.2. NADH dehydrogenase 4 (ND4) haplotypes (823 bases); total n = 300; including

numbered polymorphic sites, haplotype frequencies, shared haplotypes and indices of population

diversity for Carcharhinus amboinensis. WA = Western Australia; NT = Northern Territory;

GoC = Gulf of Carpentaria; QLD = north Queensland. Haplotypes CAMB_ND415 to CAMBND417

are unique to South African and Arabian Sea populations. GenBank accession numbers HQ393536

HQ393540 & HQ393541 – HQ393553………………………………...……………………………88

TABLE 5.3. Control region haplotypes (831 bases); total n = 300; including numbered polymorphic

sites, haplotype frequencies, shared haplotypes and indices of population diversity for Carcharhinus

amboinensis. WA = Western Australia; NT = Northern Territory; GoC = Gulf of Carpentaria;

QLD = north Queensland. Haplotypes CAMB_CR30 to CAMB_CR34 are unique to South African

and Arabian Sea populations. GenBank accession numbers HQ393554 – HQ393587………………89

TABLE 5.4. Concatenated (CN) NADH dehydrogenase 4 (ND4; 823 bases) and control region (831

bases); total n = 300; including numbered polymorphic sites, haplotype frequencies, shared

haplotypes and indices of population diversity for Carcharhinus amboinensis. Hap = Haplotype;

WA = Western Australia; NT = Northern Territory; GoC = Gulf of Carpentaria; QLD = North

Queensland; MB = Moreton Bay. Haplotypes CN46 – CN52 are unique to South African and Arabian

Sea populations………………………………………………………………………………….…….91

TABLE 5.5. MtDNA pairwise population PHIST values for Carcharhinus amboinensis (total n = 300).

a) and b) are results from ND4 and control regions individually and c) represents these regions

concatenated . GoC = Gulf of Carpentaria………………………………………...............................93

TABLE 5.6. Linearised pairwise population PHIST values of Carcharhinus amboinensis (Slatkin’s D,

Reynold’s D and M - value) (total n = 300) . GoC = Gulf of Carpentaria………………………...…94

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TABLE 5.7. Neutrality tests (Tajima’s D and Fu’s FS statistics) for individual Carcharhinus

amboinensis populations (n = 300). Sample sizes; statistics and p values are given. GoC = Gulf of

Carpentaria………………………………………………………………………………………….....95

TABLE 5.8. The population sample size (N), number of microsatellite alleles per locus (Na), average

observed heterozygosity (Ho), expected heterozygosity (He), unbiased heterozygosity (UHe), fixation

index (F) for Carcharhinus amboinensis; sample sizes for each grouping are given……………….101

TABLE 6.1. NADH dehydrogenase 4 (ND4) haplotypes (797 bases) total n = 169; including

numbered polymorphic sites, haplotype frequencies, shared haplotypes and indices of population

diversity; sample sizes for each group are given………………………………………...…………..121

TABLE 6.2. Control region haplotypes (837 bases) total n = 169; including numbered polymorphic

sites, haplotype frequencies, shared haplotypes and indices of population diversity; sample sizes for

each group are given………………………………………………………………………………....122

TABLE 6.3. Pairwise FST values between un-pooled rivers. FST values are above diagonal, p-values

are below diagonal; Significance is indicated in Bold (significance level p < 0.016 corrected for

multiple comparisons by FDR method); total n = 169…………………………………………... 124

TABLE 6.4. Concatenated mtDNA NADH dehydrogenase 4 (ND4) (797 bases) and control region

haplotypes (837 bases) total n =169, total bases = 1634; including numbered polymorphic sites,

haplotype frequencies, shared haplotypes and indices of population diversity for Carcharhinus leucas.

Nurseries (excluding group 7) are pooled by geographic distances and genetic similarities; sample

sizes for each grouping are given. Note CLEU_CN18 was only present in low frequencies in the adult

population, Darwin coastal, as such it is only included in phylogenetic reconstructions…….……...125

TABLE 6.5. MtDNA pairwise FST values between pooled nurseries for Carcharhinus leucas * (total

n = 143). Nurseries are pooled based on geographic distances and genetic similarities; sample sizes

for each grouping are given…………………………………………………………………..……...126

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TABLE 6.6. The pooled nurseries, sample size (N), number of microsatellite alleles per locus (Na),

average observed heterozygosity (Ho), expected heterozygosity (He), unbiased heterozygosity (UHe),

fixation index (F) for Carcharhinus leucas (total n = 143). Nurseries are pooled based on geographic

distances and genetic similarities; sample sizes for each grouping are given……………………….127

TABLE 6.7. Relatedness (%) within and between pooled nurseries and adult population for

Carcharhinus leucas (total n = 169) . Nurseries are pooled based on geographic distances and genetic

similarities…………………………………………………………………………………………...128

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THESIS ABSTRACT

As anthropogenic pressures including over-fishing and climate change intensify, high-order

predators are being threatened within coastal communities. Predicting consequences hinges

on our understanding of how species similarities and differences shape ecosystem function

and resilience. I compared ecological similarity between two morphologically similar

carcharhinids, bull (Carcharhinus leucas) and pigeye (C. amboinensis) sharks in northern

Australia. Ecological similarity was measured by comparing a) age structure and growth

dynamics, b) habitat use and c) genetic population structure and phylogeography.

Age structure and growth dynamics were quantified by analysing vertebrae of 199

pigeye and 94 bull sharks. Model averaging results indicated female pigeye sharks matured at

13 years, lived > 30 years and with a growth coefficient (k) of 0.145 year –1

. Male pigeye

sharks matured slightly earlier (12 years), survived > 26 years and grew faster (k = 0.161

year –1

) than females. Bull sharks matured at 9.5 years and displayed similar growth

coefficient (k = 0.158 year –1

) and survival (> 27 years) as pigeye sharks.

Analysis of vertebral microchemistry identified unique elemental signatures in each of

the freshwater bull shark nurseries, but these signatures did not occur in adult vertebrae.

Furthermore, age-specific changes in microchemistry synonymous with known ontogenetic

changes in patterns of habitat use were also evident. However, vertebral microchemistry

could not discriminate among natal habitats of pigeye sharks and age-specific changes in

vertebral microchemistry were absent. Inter-specific differences in vertebral microchemistry

suggest species display different patterns of habitat use.

Population genetic structure and phylogeography of pigeye sharks defined two

populations within northern Australia on the basis of mitochondrial DNA evidence, but this

result was not supported by nuclear microsatellite or RAG 1 markers. Phylogeography

Page | xxiv

identified three distinct clades present in differing frequencies across northern Australia. Bull

sharks displayed starkly different patterns. High genetic diversity among juveniles sampled

from different rivers supported the hypothesis of female reproductive philopatry. Furthermore,

phylogeography identified only one clade across northern Australia.

Despite similar age and growth structure, different patterns of habitat use and genetic

evolution confirm that morphological likeliness does not equate to similar ecologies in these

large carcharhinid sharks.

CHAPTER ONE – Thesis introduction

Chapter 1 – Thesis introduction

Page | 1

Environmental degradation has been steadily increasing as the global human population

expands requiring more resources. Consequently, many species are in serious decline and

their habitats are degraded (Butchart et al. 2010). For ecologists, the challenge has now

shifted beyond identifying patterns of biodiversity loss to developing a better understanding

of ecosystem dynamics and predicting consequences of such losses. Marine environments

have historically challenged social values, due to the inability of humanity to penetrate and

survive within them (Decker & O'Dor 2002). Consequently, we know relatively little of the

dynamics of these communities compared with their terrestrial counterparts. As scientifically-

acquired knowledge and technology develop, we are starting to understand just how

susceptible these habitats are to human influences and to unravel the crucial role they play in

balancing global environments (Stachowicz et al. 2007, Bracken et al. 2008).

Top predators such as large sharks form an important trophic guild in marine

environments (Stevens et al. 2000, Bruno & O'Connor 2005, Myers et al. 2007, Heithaus et al.

2009, Estes et al. 2011). Predation by these higher-order predators directly regulates prey

abundances through prey removal and also the threat of predation changes prey behaviour

and resource use among lower-order species (Schmitz et al. 2004, Frid et al. 2008, Goudard

& Loreau 2008, Heithaus et al. 2008a, Heithaus et al. 2008b, Ferretti et al. 2010). The

naturally low abundances of top-order predators due to their high demands for limited

resources increases their susceptibility to overexploitation (Heithaus et al. 2008a). It is

therefore important to understand the habitat domains of upper trophic level predators to

determine the potential impacts of declines in their abundances on ecosystems.

Much of the current modelling of ecosystem resilience, pools similar species into

groups such as those species that reside in similar habitats or are morphologically alike,

termed ‘morphospecies’ (Chin et al. 2010). Recent studies of diverse taxa have however,

questioned such methodologies given the diversity of intra- and interspecific ecologies


Page | 2

(Matich et al. 2011). Carcharhinids, such as bull (Carcharhinus leucas) and pigeye (C.

amboinensis) sharks, have historically been mis-identified due to their morphological

likeliness and share similar habitats (Last & Stevens 2009). As such, they provide an

excellent model to examine the appropriateness of pooling such species in ecological

assessments.

Bull and pigeye sharks are both described as having a short, broad and blunt snout,

small eyes, no inter-dorsal ridge and triangular saw-edged teeth (Compagno 1984, Cliff &

Dudley 1991a, Cliff & Dudley 1991b). Furthermore, both sharks are common in shallow

coastal tropical and sub-tropical waters (Last & Stevens 2009). This morphological similarity

combined with similar distributions has led to common mis-identifications and the

assumption of ecological similarity (Cliff & Dudley 1991b). Evidence for complex patterns

of habitat use are beginning to question whether these two species are truly ecologically

similar (Brunnschweiler et al. 2010, Carlson et al. 2010, Karl et al. 2011, Knip et al. 2011a,

Knip et al. 2011b). Extensive acoustic tracking of bull sharks has described intra-specific

size-based habitat partitioning and evidence of broad-scale migrations (Heupel et al. 2010b,

Curtis et al. 2011, Werry et al. 2012). Comparatively little research has mapped such

movements in pigeye sharks. However, recent studies suggest older juvenile pigeye sharks

display a greater affinity to deeper waters than young-of-the-year; although sample size of

larger individuals in this study was limited (Knip et al. 2011a).

Direct comparisons of ecological similarity between these morphospecies have not

been investigated. It is also unclear whether these complex patterns of habitat use contribute

to similar migration rates between populations within their known distributions i.e.

population genetic structure, and implications for species susceptibility to future geological

changes in coastal environments. This thesis investigates whether bull and pigeye sharks

display similar habitat affinities and life history / population structure contributing to similar


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ecological roles and species susceptibility to both environmental stocasticity and

anthropogenic impacts.

Bull sharks are born between 550 – 800 mm total length and reach maximum lengths

of 3400 mm in Australia (Compagno 1984, Cliff & Dudley 1991b, Last & Stevens 2009).

Females mature later than males (2300 and 2200 mm respectively within Australia) (Stevens

& McLoughlin 1991, Last & Stevens 2009); age at maturity has not been defined in Australia

but differs among regions ranging from 9 – 18 years (Branstetter & Stiles 1987, Wintner et al.

2002, Cruz-Martinez et al. 2004, Neer et al. 2005). Maximum age can vary, but is estimated

to be between 30 to > 50 years (Branstetter & Stiles 1987, Wintner et al. 2002, Cruz-Martinez

et al. 2004, Neer et al. 2005).

Bull sharks are one the few elasmobranchs capable of residing in freshwater, estuarine

and marine environments (Thorson 1972). Freshwater / estuaries provide low mortality

environments for juveniles where they reside for approximately four years before moving

into higher salinity environments with the onset of maturity (Heupel & Simpfendorfer 2008,

Thorburn & Rowland 2008, Yeiser et al. 2008, Last & Stevens 2009). Young-of-the-year are

most abundant in freshwater; as an individual grows and their body size : surface area ratio

increases, habitat preferences change to higher salinity environments reducing osmotic

pressures (Heupel & Simpfendorfer 2008, Curtis et al. 2011). Frequency and duration of

excursions into marine areas increases with size until maturity, at which point the preferred

habitat shifts from predominately freshwater to marine (Yeiser et al. 2008). Diel variation of

juveniles within freshwater / estuaries also reflects dissolved oxygen (DO2) and temperature

gradients (Ortega et al. 2009, Heupel et al. 2010b). Mature bull sharks (> 2m total length)

found in rivers are rarely male (Montoya & Thorson 1982, Snelson et al. 1984, Last &

Stevens 2009), suggesting females use this part of their range for parturition. Anecdotal

evidence suggests that litter sizes range from 1 - 13 pups and gestation is thought to be 10 –


Page | 4

11 months (Snelson et al. 1984, Stevens & McLoughlin 1991, Last & Stevens 2009).

Periodicity of the reproductive cycle is still unknown but hypothesised to be biennial

(Rasmussen & Murru 1992, Cavanagh et al. 2003, Last & Stevens 2009).

Tracking (acoustic and pop-up satellite) studies and directed surveys have confirmed

the inshore affinity of mature bull sharks although broad-scale movements have been

recorded (Brunnschweiler et al. 2010, Carlson et al. 2010) suggesting high connectivity

between coastal populations. Population genetic structure has been defined in the western

Atlantic. Population structure evident in mitochondrial DNA that was not present in

microsatellite DNA suggests that female bull sharks maybe philopatric (Karl et al. 2011).

Stomach content analysis has confirmed that bull sharks feed on a diverse selection of prey

including teleost, crustacean, cephalopods, reptiles and other elasmobranhcs (Stevens &

McLoughlin 1991, Werry et al. 2012). Stable isotope (δ13

C and δ15

N) analysis has identified

ontogenetic shifts in diet and trophic position, and further evidence suggests that bull sharks

maybe individual specialists rather than true generalists (Matich et al. 2010, 2011, Werry et al.

2012).

The bull shark is classified by the IUCN as ‘Near Threatened’ due to rapid

modification of freshwater and estuarine nurseries reducing juvenile survival and the high by-

catch recorded in many different coastal fisheries (Salini et al. 2007, IUCN 2010). Heavy

fishing pressure (both targeted and by-catch) in the United States of America has raised

questions as to the sustainability of current catches (Baum et al. 2003, Burgess et al. 2005,

Baum & Worm 2009). Furthermore, large declines in the size of animals caught off South

Africa have also been recorded, highlighting the impacts of size-selective fishing pressures

on population structure and resilience (Dudley 1997, Dudley & Simpfendorfer 2006).


Page | 5

Despite occupying similar habitats and being subjected to similar fishing and coastal

development pressures, little is known about the basic biology and ecology of the pigeye

shark compared to the bull shark. This has led to the IUCN’s Red List classification of ‘Data

Deficient’, flagging the need for research to address these knowledge gaps (IUCN 2010). The

morphological similarity to the bull shark has led to multiple field misidentifications (Cliff &

Dudley 1991a); as such, much of the species’ range is still undefined. Pigeye sharks are born

between 600 – 650 mm and obtain slightly smaller total lengths than bull sharks (2800 mm in

Australia) (Compagno 1984, Last & Stevens 2009). Like bull sharks, females mature larger

than males (2150 and 2100 mm respectively) (Stevens & McLoughlin 1991, Last & Stevens

2009); age at maturity is not known. Catch data indicate similar litter sizes and gestation

periods as bull sharks (Stevens & McLoughlin 1991, Last & Stevens 2009). Tracking data

reveal that juveniles of this species inhabit inshore regions adjacent to creeks and rivers

mouths, while larger individuals prefer deeper coastal waters (Last & Stevens 2009, Knip et

al. 2011a). Also, changes in salinity influence fine scale movement patterns within coastal

embayments (Knip et al. 2011b). The KwaZulu-Natal Sharks Board has recorded serious

declines in the catch rates of this species between 1978 – 1998 suggesting that this species

cannot sustain heavy, localised fishing (Cliff & Dudley 1991a, Dudley & Simpfendorfer

2006). This species is targeted and taken as by-catch in Australian commercial fisheries,

stressing the need for a better understanding of their demography (Handley 2010).

The marine ecosystems of northern Australia are less degraded than in many other

global locations (Halpern et al. 2008) enabling research on populations in relatively pristine

conditions. Ecological similarity of these species will be compared by: 1) quantifying growth

rate, maximum age and age at maturity; 2) comparing inter- and intra- specific habitat

partitioning and 3) comparing population genetic structure tracing whether movement

patterns are different over an evolutionary time-scale. Both bull and pigeye sharks are


Page | 6

expected to display similar age and growth structure given the comparable maximum

recorded total lengths obtained and reproductive strategies of both species. Despite known

differences in habitat use as neonates (young-of-the-year bull sharks residing in freshwater),

larger juvenile bull sharks are predicted to coexist in shallow coastal embayment’s with

younger juvenile pigeye sharks. This sympatry is expected to also be evident among adults of

each species also coexisting in coastal environments. Furthermore, these similar patterns are

predicted to extend to migration (gene flow) between populations resulting in similar

population genetic structure.

CHAPTER TWO – The application of genetic

tools to investigate reproductive philopatry in sharks.

A version of this Chapter has been published as a section in: Dudgeon, C.L. , Blower, D.C., Broderick,

D., Giles, J.L., Holmes, B.J., Kashiwagi, T., Krück, N.C., Morgan, J.A.T., Tillett, B.J. and Ovenden,

J.R. (2012). A review of the application of molecular genetics for fisheries management and

conservation of sharks and rays. Journal of Fish Biology. 80:1789-1843. DOI: 10.1111/j.1095-

8649.2012.03625.

Chapter 2 – Reproductive philopatry in sharks `

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WHAT IS PHILOPATRY

Philopatry describes an absence of dispersal (Sheilds 1982); that is when propagules capable

of movement remain in or stay in close proximity to their natal areas (absence of natal

dispersal) or previously successful breeding sites (absence of breeding dispersal) restricting

gene flow (Mayr 1963). First used in reference to seed dispersal in passive dispersers such as

insect-pollinated plants (Bateman 1950), philopatry is also evident in active dispersers such

as birds, mammals and recently reptiles and fish (Greenwood 1980, Dittman & Quinn 1996,

FitzSimmons et al. 1997, Freedberg et al. 2005, Dubey et al. 2008).

One of the first and arguably most prominent examples of philopatry in active

dispersers is the mating system of migrating birds (Austin 1949, Greenwood 1980). Here,

birds seasonally migrate long distances to feeding grounds or other ecologically favourable

habitats (evidencing their high dispersal potential), but instead of dispersing, return to

previous successful nesting grounds to breed (Greenwood 1980, Greenwood & Harvey 1982).

Site fidelity to, or tenancy of these breeding sites despite dispersal potential, is philopatric

behaviour. Site fidelity however, is not always indicative of philopatry although often

synonymously used to describe this behaviour. Species with limited dispersal potential can

still display prolonged dependency for certain areas including feeding grounds, but are not

philopatric because they are not capable of dispersing further (Green et al. 2012). In the

above example, the designation of philopatry results from extensive breeding site surveys in

which large numbers of adult birds and their offspring were banded providing long-term

individual identification. Mating behaviours and prolonged dependency to breeding sites

were then quantified by directly counting the departure and arrivals of individuals over

consecutive breeding cycles which were then interpreted to determine population-level

genetic structure and gene-flow (Austin 1949, Greenwood & Harvey 1982). Until advances in


Page | 8

genetic and tagging technologies, these breeding behaviours were hard to discriminate in less

observable species, such as fish.

The advent of Polymerase Chain Reaction and the rapid replication of targeted gene

regions revolutionised assessments of population genetic structure and dispersal (Avise 1994).

No longer were behavioural studies limited to one or two sites which had to be physically

observed at individual levels in long-term studies to confirm prolonged site affinities. Rather,

small tissue samples could be collected from individuals over a species entire range and the

appropriate gene regions amplified and replicated to infer population-level genetic structure

and dispersal (Freeland 2005). Furthermore, each individual only needs to be sampled once.

However, in the same way that site fidelity does not necessarily demonstrate

philopatry; the identification of population genetic structure does not necessarily confirm

philopatry (Duncan et al. 2006). Population genetic structure commonly results from

restricted gene flow (limited dispersal) due to the presence of physical barriers (Avise 1994).

The designation of philopatry requires the absence of dispersal despite potential (Mayr 1963),

commonly identified by observing an individual leaving and returning to breeding sites

(Sheilds 1982, Bernstein et al. 2007), the presence of at least some individuals in distant

locations or through the identification of sex-biased dispersal (one sex represents dispersal

potential and the other represents the absence of dispersal or philopatry) (Feldheim et al.

2004, Portnoy et al. 2010). In theory, for a species to be classified as philopatric, both sexes

should participate in dispersal (Sheilds 1982); however, this behaviour is commonly biased

towards one sex rather than the other. In birds, males are often philopatric with female-

mediated dispersal whereas the opposite pattern is common mammals (Greenwood 1980,

Feldheim et al. 2004, Dethmers et al. 2006, Portnoy et al. 2010, Karl et al. 2011).


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IDENTIFICATION OF PHILOPATRY IN SHARKS

The designation of philopatry was originally limited to pronounced species

behaviours, such as homing patterns in anadromous salmon (Dittman & Quinn 1996, Hueter

et al. 2005), because of the challenges of observing movement patterns of marine animals

compared with their terrestrial counterparts. However, with the advances in tracking and

genetic technologies, including genetic tagging, examples of less obvious philopatric

behaviour, such as occurs in sharks, are becoming increasingly evident (Hueter et al. 2005).

Philopatry in sharks has been identified in white, Carchardon carcharias (Pardini et

al. 2001, Bonfil et al. 2005, Jorgensen et al. 2010), lemon, Negaprion brevirostris (Feldheim

et al. 2004), bull, Carcharhinus leucas (Karl et al. 2010, Tillett et al. 2012b), sandbar, C.

plumbeus (Portnoy et al. 2010) and blacktip, C tilstoni (Keeney & Heist 2006) sharks and is

commonly more prevalent in females than males. Philopatric females however are not

necessarily weaker dispersers. Females may be returning to specific nurseries to pup, but

might travel large distances to find a suitable mate or to feed. Mammals also display male

mediated dispersal and female philopatry; birds however show the reverse pattern

(Greenwood 1981, Greenwood & Harvey 1982). Current studies suggest that reptiles also

show similar patterns as mammals although this behaviour has not been observed in enough

species to form generalisations (Freedberg et al. 2005, Dubey et al. 2008).

The occurrence of philopatry has largely been associated with the evolution of social

structure within populations (Shields 1982). Individual fitness is presumably higher from

remaining in close proximity to their relatives or previously successful breeding sites

(Greenwood 1980). Such social order was not thought to exist in fish and other marine

species, consequently it was thought that philopatry would be rare in this group. However,


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there is growing evidence for the occurrence of this behaviour in these species (Dittman &

Quinn 1996, Hueter et al. 2005).

The first accounts of philopatry in sharks were reported in lemon sharks (Negaprion

brevirostris) by Feldheim et al. (2001a) using kinship and parentage analyses, and in white

sharks (Carcharodon carcharias) by Pardini et al. (2001) using population genetic structure

assessments. At Bimini Island in the Bahamas, a number of gravid female lemon sharks were

identified (through microsatellite DNA and Personal Identification Tags) in the same nursery

in consecutive years. Subsequent kinship and parentage analysis of microsatellite DNA

confirmed these females gave birth to multiple litters over consecutive breeding cycles

demonstrating female site fidelity to nurseries. Kinship and parentage analyses are powerful

methods for determining parentage and the number of sires per litter, but alone it does not

demonstrate philopatry because it cannot assess dispersal potential. Unlike the earlier bird

migration example, dispersal in sharks is not easily observed as discussed by Speed et al.

(2010). However, philopatry in lemon sharks was confirmed by combining evidence for site

fidelity to nurseries with evidence of high gene flow (ie high dispersal) from global

population genetic structure studies (Feldheim et al. 2001b) plus sex-specific differences that

were identified in the parentage of litters suggesting differences in dispersal between genders

(Feldheim et al. 2004).

Parentage and kinship studies are most appropriate for discerning philopatry if adults

and juveniles from breeding sites / nurseries can be sampled. Other advantages of parentage

and kinship analyses include increased power to discriminate whether observed behaviours

are the “exception” or the “rule”. If female philopatry only occurs in a small proportion of the

population, it is less likely to be identified through population structure assessment (discussed

below). Whereas, if sample size is sufficiently large, kinship and parentage analysis can


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identify the proportion of population displaying this behaviour providing valuable biological

information (Verissimo et al. 2011).

Pardini et al. (2001) used different modes of inheritance of genetic markers

(microsatellite and mitochondrial) to infer gender-biased philopatry in white sharks. Unlike

microsatellites that are inherited from both parents, mitochondrial DNA (mtDNA) is only

maternally inherited (Avise 1994). They concluded that as genetic structure was evident in

mtDNA (indicative of female site fidelity) but absent in microsatellite DNA (representing

dispersal potential), females were more likely to be philopatric than males. Even without the

microsatellite data, the presence of mtDNA population genetic structure plus the known

intercontinental movements of white sharks from tagging and tracking studies was evidence

that the species was likely to be philopatric.

Care needs to be exercised when discussing gender-biased dispersal from genetic data,

as ecological and evolutionary constraints can also account for the genetic results. The solely

maternal inheritance of mtDNA will cause genetic fixation, due to reproductive isolation, to

occur faster and remain in mtDNA longer than in nuclear markers (Birky et al. 1983).

Furthermore, higher levels of genetic diversity are generally found in microsatellites and can

result in lower FST (measure of population differentiation) values relative to mtDNA markers

(Hedrick 1999; Frankham et al. 2002). As such, the mere presence of structure in mtDNA

that is absent from microsatellite data is not sufficient to conclude female philopatry. Rather,

statistical analyses quantifying either the power of selected loci to detect population structure

if present (Dudgeon et al. 2009), or multiple coalescence analyses quantifying sex-biased

gene-flow explaining the cause of observed population structure differences; not just that

population structure differences exist, are needed (Portnoy et al. 2010).


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Discerning philopatry using variation in population genetic structure is heavily

influenced by sampling strategy. For example, if females remain in an area for a given time

and then disperse, the identification of philopatry will depend on whether the philopatric or

dispersed phases are sampled. Distinguishing this behaviour genetically is complex and

provides an example of where a holistic approach (including other tagging methods, such as

acoustic or satellite tagging) would complement genetic analyses. For example, Bonfil et al.

(2005) suggested that the philopatric signature identified in female white sharks (Pardini et al.

2001) conflicted with large scale acoustic and satellite tracking studies that showed female

white sharks were moving between locations. Rather than viewing these results as

contradictory, the combined results indicate either limited gene-flow between study regions

with the number of migrants insufficient to mix population genetic structure (migration

between populations is a rare event), or that individuals are returning to key areas and the

identified genetic structure is due to sampling while individuals were breeding rather than

dispersing.

Blower et al. (In Press) also compared population genetic structure in mtDNA and

microsatellites mostly in juvenile white sharks, although they only examined Australian

populations (east and south-western coasts). This study, unlike Pardini (2001), found weak

but significant structure in the microsatellites that was also evident in the mtDNA. They

concluded that both males and females are philopatric on a continental scale; introducing a

new component in the discrimination of philopatry (i.e. scale).

To date, the designation of philopatry in sharks has been predominately made by

genetic assessments, however this is not the only method that could be used. In the example

of bird philopatric analyses, colony sites can be identified by large aggregations of birds.

Mate selection, egg gestation and post-natal care often occur in the same location over a few

months. Also, individuals can be banded providing long-term individual identification.


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Reproduction and gene flow can be quantified through observing adult mating behaviour and

tracing subsequent returns of offspring. Extensive site surveys can then record the arrival of

individuals (including banded hatchlings), associated population dynamics and reproductive

successes over consecutive breeding cycles (Austin 1949, Greenwood 1980). For sharks,

these behaviours are less easily observed. Gestation periods are often greater than 12 months

and parturition is rarely observed; although nurseries have been described (Heupel et al. 2007,

Whitney & Crow 2007, Diatta et al. 2008, Romine et al. 2009). Also, sharks are commonly

long-lived and occupy large home ranges (Campana et al. 2002, Heupel et al. 2004). As such,

resights in mark recapture studies (such as those used in the bird migration example) are low,

and behaviour of mate pairs and their offspring are hard to physically observe and trace.

Satellite and acoustic tracking studies are capable of providing dispersal potential. For

example, satellite archival tags confirmed adult bull sharks do indeed display affinities to

shallow coastal waters, however some individuals were tracked migrating over 1500 km in

the Gulf of Mexico demonstrating their capacity for dispersal (Brunnschweiler et al. 2010;

Carlson et al. 2010).

Unfortunately, tags cannot discriminate if tracked individuals remain in a population

and reproduce (i.e. disperse). Furthermore, the battery life of archival tags (~ 3 years) (Kohler

& Turner 2001) is significantly shorter than attainment of sexual maturity (> 7 years)

(Cailliet & Goldman 2004) limiting their use for determining whether neonates eventually

breed in their own natal areas and tag life is also shorter than shark lifespans (> 20 years)

limiting their use tracking long-term breeding site fidelity (Simpfendorfer & Heupel 2004,

Speed et al. 2010). Archival tags can however provide additional fine scale information at a

metapopulation scale, such as if individuals are reutilising exact locations or deviating to

similar proximal areas.


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Genetic assessments overcome many of these limitations as gene flow is directly

quantified without having to observe actual mating behaviours, patterns are described in

either evolutionary (population genetic structure assessments) or generational (kinship /

parentage analyses) time scales. At least for fisheries species, large number of samples can be

obtained compared to smaller sample sizes used for physical tagging. Genotyping multiloci

microsatellite signatures also provides a permanent record of each individual, but the power

of these signatures to identify individuals strictly depends on the number of loci and the

degree of polymorphism of the available markers (Balloux et al. 2000, Schultz et al. 2008).

And lastly, information encoded in the genetic data (irrespective of method or marker)

provides information beyond that provided by each individual themselves, either through

tracing relatedness or population-level genetic variation.

EVOLUTION OF PHILOPATRY

Many evolutionary theories have been proposed to explain the occurrence of philopatry in

birds and mammals (Shields 1982). These include the dominance of one sex, avoidance of

inbreeding, or population density although few hypotheses are consistent across different

species (Greenwood & Harvey 1982). Arguably, the best predictor for the occurrence of

philopatry is mating systems (Greenwood & Harvey 1982). Monogamy, male philopatry and

female-biased dispersal are most likely to occur in species where males defend a territory or a

resource and breeding success (reproductive fitness) is determined by female selection of

male partners based on the quality of the resource or territory. Whereas, polygamy, female

philopatry and male-mediated dispersal are likely to occur in those species where females do

not select male partners, rather breeding success (reproductive fitness) is determined by male

access to females (Greenwood 1980, Shields 1982).


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In sharks, female reproductive costs associated with egg development and gestation of

large offspring is greater than for males (Hueter et al. 2005, Portnoy et al. 2010).

Consequently, females are more likely to benefit from increased juvenile survival associated

with re-utilising successful nurseries, and thus be philopatric. Furthermore, polyandry is

increasingly being identified in sharks consistent with the above hypothesis of the female

philopatry in species where reproductive fitness is shaped by male access to females

(Feldheim et al. 2001a, Daly-Engel et al. 2006, Diabattista et al. 2008, Portnoy et al. 2010).

Reproductive strategies are diverse in sharks and consequently philopatry is not

expected to occur in all species (Carrier et al. 2004, Hueter et al. 2005). For example, despite

female Australian sharpnose sharks, Rhizoprionodon taylori having higher reproductive cost

than male conspecifics, female philopatry is not predicted in occur this species. These small,

coastal sharks are likely to have limited dispersal compared with larger species and are

therefore more likely to display site fidelity to coastal areas but are not classified as

philopatric because they cannot disperse further. Also, smaller offspring in relation to average

female body size are produced more frequently than other larger species of sharks indicative

of comparatively lower female reproductive costs reducing benefits of philopatry.

Furthermore, there is no evidence of size-based habitat partitioning which reduces intra-

specific competition, increasing juvenile survival (Knip et al. 2010; Heupel & Simpfendorfer

2011). As such, philopatric behaviour improving juvenile survival and female fitness is less

likely to have evolved.

Understanding the evolution of philopatry in sharks requires the further division of

this behaviour into natal and breeding philopatry, in accordance with whether dispersal is

natal or breeding. Here, natal philopatry refers to the repeated use of an individuals’ own

natal areas to breed, while breeding philopatry is the repeated use of previously successful

breeding site that are not necessarily an individual’s own natal area (Lindberg et al. 1998).


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There is reference to these terms in the literature, however they often have mixed definitions

and have been used to describe the behaviour of juveniles not spawning individuals. ‘Natal

philopatry’ has been used to describe the delayed dispersal by juvenile dolphins from nursery

areas (McHugh et al. 2011). This scenario is best described as ‘site fidelity’ rather than

‘philopatry’, as referred to by Chapman et al. (2009) describing the same behaviour in

juvenile lemon sharks. Neither of these studies discusses dispersal potential or breeding

behaviour of this species.

Other examples of confusing terminology is the use of ‘natal philopatry’ without

clarifying whether prolonged dependencies to successful breeding sites are indeed their own

natal areas (Sheridan et al. 2011). In sharks, natal philopatry has not yet been described,

however, ongoing research programs at Bimini Island in the Bahamas have genotyped a

substantial number (~ 900) of juvenile lemon sharks providing a permanent record of each

individual (and relatives). If any of these individuals (or relatives) reappear in 12 – 13 years

(estimated age at maturity, Chapman et al. 2009), they can be identified and will evidence

natal philopatry in sharks.

CONCLUSION

As shark tagging technology develops, there is growing evidence of the diverse and complex

nature of elasmobranch habitat use. The time-frame for spatial, habitat use research has been

extended from one year (numbered tags with poor durability) to typically three years

(electronic tags) and up to ten years and possibly more for genetic tags. These tools are

improving our ability to define nurseries, home ranges and dispersal abilities which allow us

to identify reproductive philopatry in sharks. The philopatric nature of many species (i.e.

birds, turtles and anadromous salmon) stem from targeted assessments and detailed study of


Page | 17

breeding aggregations (e.g. nesting or spawing sites). Individually tracking sharks in similar

numbers (for example, as early mark re-capture studies to determine population-level gene-

flow) is unrealistic as they are highly mobile and long-lived and cannot be easily observed or

handled. As such, the common methodology to discern philopatry for sharks measures

genetic variation (both at an individual and population level) across a species entire range to

infer breeding behaviour.

The complex nature of this behaviour is best investigated using a combination of

methods. The value of genetic methods in their basic form is that through genotyping, an

individual is permanently ‘tagged’ and therefore research studies become no-longer limited

by the physical properties of tagging methods i.e tag attachment or battery life. The

information encoded in the genetic data, irrespective of method or marker, provides

information of the long-term consequences of movement patterns through tracing genetic

inheritance. This provides greater information beyond that provided by each individual

themselves. Caution must be taken when interpreting the data as there are many scenarios

that may shape movement patterns and their genetic representation. Sampling strategies

targeting nurseries, and grouping analyses by sex and size increases the power for discerning

reproductive philopatry.

For species with two distinct life phases (nursery-bound and dispersed), the

importance of protecting juveniles within shark nurseries is widely acknowledged (Heupel et

al. 2007). The increasing discovery of sex-biased dispersal and reproductive philopatric

behaviour in sharks changes our understanding of their biology, including ecological function

and how fisheries’ catch and other regional mortalities impact on species resilience (Hueter et

al. 2005, Speed et al. 2010).

CHAPTER THREE - Similar life history traits

in bull (Carcharhinus leucas) and pigeye (C.

amboinensis) sharks

Published: Tillett, B.J., Meekan, M.G., Field, I.C., Hua, Q. and Bradshaw, C.J.A. (2011). Similar life

history traits in bull (Carcharhinus leucas) and pigeye (C. amboinensis) sharks. Marine and

Freshwater Research. 62: 850-860. DOI:10.1071/MF10271

Chapter 3 – Comparative age structure and growth dynamics

Page | 18

INTRODUCTION

Growing global populations and unsustainable fishing practices threaten species diversity in

tropical ecosystems. Appropriate ecosystem-based management requires an understanding of

how species’ ecological similarities and differences shape ecological function. Age structure

and growth dynamics (longevity, age at maturity and growth rate) are important factors

shaping species’ ecological function and provide estimates of species resilience and

susceptibility to over-exploitation (Simpfendorfer et al. 2002, Cailliet & Goldman 2004,

Cailliet et al. 2006). Furthermore, quantifying these parameters enables management agencies

to identify the contribution of different cohorts to species persistence and ensure that

dominant cohorts receive special emphasis or protection, reducing the risk of over-

exploitation (Campana 2001, Cortes 2002, Otway et al. 2004). Age structures and survival of

key cohorts can also be monitored over time with any evidence of change used as an indicator

of the impact of increased exploitation pressure (Stevens 2002, Bradshaw et al. 2007, Field et

al. 2009a).

Here, we compare the age structures and growth dynamics of two sympatric and

morphologically similar carcharhinids, bull (Carcharhinus leucas) and pigeye (C.

amboinensis) sharks, both common apex predators in shallow tropical and sub-tropical

coastal waters (Last & Stevens 2009). The proximity of many of their habitats to human

influences (coastal development, run-off sources) and their susceptibility to fishing (via

targeted fisheries or as bycatch) highlights the need for a better understanding of their life

history traits. Furthermore, the paucity of good demographic data combined with the high

frequency of human interactions has led to the listing of the bull shark by the IUCN as Near

Threatened (FAO 1999, Salini et al. 2007). Although occupying similar habitats, the pigeye

shark is listed by the IUCN as Data Deficient, emphasising the need for a better definition of

life history traits to facilitate an accurate assessment of population viability. The pigeye shark


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is thought to be common throughout the Indo-west Pacific Oceans, although the full extent of

its range is unclear because it might be frequently misidentified in catches (Last & Stevens,

2009).

For bull sharks, age and growth estimates are available from South Africa and the

northern and southern regions of the Gulf of Mexico (Branstetter & Stiles 1987, Wintner et al.

2002, Cruz-Martinez et al. 2004, Neer et al. 2005). These suggest that bull sharks, like many

other carcharhinids, are long-lived, attaining maximum ages between 30 to > 50 years.

Females typically grow to larger sizes and mature later than males, and growth rates for both

sexes are fastest during the juvenile stage. There are, however, differences in maximum age

estimates and growth coefficients among locations, suggesting separate stocks within

populations. In contrast, there have been no studies estimating age and growth parameters of

pigeye sharks.

The present study is the first to examine the age structure and growth dynamics of

bull and pigeye sharks within Australia. Previous work in the region has provided estimates

of ranges of size and fecundity for these species, although sample sizes were small and based

on limited catch data (Stevens & McLoughlin 1991, Stevens 2002). We use the growth

increments within vertebrae of 199 pigeye and 94 bull sharks to first estimate and compare

age and growth parameters (estimates of longevity, age at maturity and growth rate); second,

to verify annual deposition on growth band in pigeye sharks and third, determine whether

variability in birth size influences parameter estimates of age and growth. Finally, we

consider our results in the context of the resilience and susceptibility to over-exploitation of

these species.

Ch

apte

r 3 –

Co

mp

arative age

structu

re an

d gro

wth

dyn

amics

FIG. 3.1. Capture locations for the pigeye (Carcharhinus amboinensis; n = 199) and bull shark (Carcharhinus leucas; n = 94) across northern Australia.


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MATERIALS AND METHODS

VERTEBRAL COLLECTION

Vertebrae of bull and pigeye sharks were sourced principally from the catches of the

commercial Northern Territory Offshore Net and Line Fishery (NTONL) and from bycatch of

the Barramundi Fishery (NTBarr) operating along the Northern Territory coastline during

2007 – 2009 (Fig. 3.1). The NTONL Fishery deploys long-lines and pelagic nets. Long-lines

must not exceed 15 nautical miles with no more than 1000 snoods (hooks), and nets must be

1000 – 2500 m long with a square mesh size of 150 – 250 mm and a drop of 50 – 100 meshes.

The NTBarr Fishery used nets between 50 – 500 m in length with a square mesh size of

150 – 175 mm with a 16 mesh drop. In addition, several fishery-independent surveys were

conducted in freshwater, estuarine and marine environments around Darwin including

Darwin Harbour, and the Daly, Wildman, West Alligator, South Alligator and East Alligator

Rivers (Fig. 3.1). In these surveys, animals were caught either using long-lines or nets. Long-

lines were approximately 50 m long with 50 snoods (hook size 11/0) positioned 1 m apart,

and nets were approximately 50 m long with a square mesh size of 150 - 250 mm with a

16-mesh drop. Both were weighted and deployed along the bottom in depths ranging from

5 – 15 m. Sample sizes from each of these locations are given below (Table 3.1).


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TABLE 3.1. Sampling details for fishery independent survey’s (species combined). Location, sample size

(sex ratio), species, average total length (TL) ± standard deviation (mm), capture date (months / years).

Location

Sample size

(sex ratio)

Species

Average TL (±

SD) (mm)

Capture date

Darwin harbour 4 (2 m, 2 f) C. amboinensis 753.75 (43.28) March 2008

Daly R 23 (16 m, 7 f) C. leucas 866.91 (123.03) July / August 2007

Wildman R 4 (3 m, 1 f) C. leucas 742 (18.96) November 2007

West Alligator R 0 - 0 November 2007

South Alligator

R 0 - 0 November 2007

East Alligator R 20 (9 m, 11 f) C. leucas 759.45 (27.45) April 2008

East Alligator R 1 (0 m, 1 f) C. leucas 645 (0) November 2007

In total, 199 pigeye and 94 bull sharks were sampled. All individuals were sexed based on the

presence or absence of claspers and where possible, their total weight (TW), total length (TL)

and fork length (FL) was measured. Age structure and growth dynamics are based on total

length (TL). Small animals (< 1 m TL) were examined for the presence of umbilical scars as

an indication of time since birth. For each captured individual, a section of approximately 15

vertebrae were removed from beneath the first dorsal fin origin and stored frozen. Owing to

the morphological similarities between members of the genus Carcharhinus, a small tissue

fragment (approximately 1 g) from each individual was also collected and tested genetically

to confirm species identity.

VERTEBRAL ANALYSIS

Vertebral samples were defrosted and excess tissue, neural and haemal arches were excised,

exposing the centra. Individual centra were then separated and any connective tissue removed

with a scalpel blade and Milli-Q water. Polished centra were then left to air dry causing any


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remaining tissue to become brittle and peel away. Cleaned vertebrae were weighed and

embedded in a two-part epoxy resin. Sagittal sections were cut through the core using a low-

speed diamond saw (Isomet, Buehler) at approximately 240 rpm with 250 g-load weight.

Sections were ground on wet and dry paper until ~ 0.4 mm thick and rinsed again in Milli-Q

water to remove contaminants. They were then mounted on glass slides using a neutral

mounting medium (CrystalbondTM

509, Queensland, Australia).

Sections were viewed under a Leica DM 400B compound light microscope connected

to a video camera (C3, jAi, Japan). The image was analysed using the computer imaging

package OPTIMAS 6.5 (Cybernetics 1999). Age was estimated by counting growth bands

(defined as one calcified, opaque ring and one less-calcified, translucent ring) visible along

the corpus calcareum reading from the focus to the outer centrum edge (Fig. 3.2). The angle

of change caused by differences in growth rate from intra-uterine to post-natal life history

stages was regarded as the point of birth, or birth mark, and recorded as year zero. Two

readers made three non-consecutive counts. Readers were unaware of the total length or sex

of specimens and each count was given a confidence rating from 1 (low confidence)

to 3 (high confidence). Count reproducibility, as indicated by within and between reader

variability, was determined by calculating an index of average per cent error (IAPE)

(Beamish & Fournier 1981, Campana et al. 1997) and the coefficient of variation (CV)

(Chang 1982, Campana et al. 1997).


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FIG. 3.2. Sagittal section of thoracic vertebrae showing growth increments and terminology. Bull shark

(Carcharhinus leucas; 10 years old).

VALIDATION AND VERIFICATION

Deposition of annual bands in bull shark vertebrae has been verified by earlier studies from a

number of different regions (Branstetter & Stiles 1987; Wintner et al. 2002; Cruz-Martinez et

al. 2004; Neer et al. 2005). Consequently, we assumed that growth bands were deposited

annually in the bull sharks we examined. As ours is the first study to estimate age and growth

parameters for the pigeye shark, there have been no validation studies of band deposition

patterns in the vertebrae of this species.


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RADIOCARBON ANALYSIS

Thermo-nuclear bomb testing began in the late 1950s to early 1960s, and elevated 14

C

concentrations peaked in the atmosphere in the mid-1960s (Hua & Barbetti 2004). Via air-sea

gas exchange, the excess 14

C was transferred to the ocean’s surface leading to an increase in

oceanic 14

C, which reached its maximum ~10 years later than atmospheric 14

C (Druffel &

Suess 1983, Nydal & Gislefoss 1996). Since then, the 14

C level in the ocean’s surface has

been decreasing as bomb radiocarbon has penetrated deeper into the ocean. By comparing

14C concentrations present in the known age shark vertebrae with a

14C bomb curve from

surrounding ocean waters, the date of vertebral matrix deposition can be determined and can

then be used to validate age estimates from increment counts (Campana et al. 2002,

Ardizzone et al. 2006, Kerr et al. 2006, Francis et al. 2007).

Six pigeye shark vertebrae were cleaned with deionised water to remove surface

contamination. The birth band was then removed and ground to a fine powder, ~ 6 - 19 mg of

which was used for bomb radiocarbon analysis. Following the method described in

Ardizzone et al. (2006) and Kerr et al. (2006), each birth band sample was pre-treated to

remove inorganic carbon using 0.25M HCl at refrigerator temperatures. After 2 hours, dilute

acid solution was decanted and fresh dilute HCl was added into the sample vial, which was

then placed in a fridge overnight to complete the demineralisation reaction. The organic

residue was washed thoroughly with deionised water, and then transferred into a Vycor

combustion tube. All water attached to the residue was removed using a vacuum pump. The

pre-treated sample was combusted to CO2 and converted to graphite following the methods

described by Hua et al. (2001). Radiocarbon analysis was carried out using small tandem

applied research (STAR) accelerator mass spectrometry (AMS) with a typical precision of

0.4% (Fink et al. 2004).


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Reference bomb curves from the published literature appropriate to sea water around

northern Australia were determined using local/regional radiocarbon marine reservoir

corrections (ΔR; Table 3.2).

TABLE 3.2. ΔR values in northern Australia were used for the calculation of average regional ΔR value of

63 ± 60 yr for this region shown in Fig. 3.3. All data were taken from the on-line ΔR database of Reimer &

Reimer (2001).

Location Longitude Latitude ΔR ± 1σ

(14

C yr)

References

Gulf of Carpentaria 141ºE 12ºS -14 ± 60 Rhodes et al., 1980

Gulf of Carpentaria 141ºE 12ºS 125 ± 60 Rhodes et al., 1980

Torres Strait, Australia 143ºE 10ºS -4 ± 84

Gillespie 1977;Gillespie

and Polach, 1979

Torres Strait, Australia 143ºE 10ºS 61 ± 85


and Polach, 1979

Torres Strait, Australia 143ºE 10ºS 78 ± 68


and Polach, 1979

Raffles Bay, Northern

Australia 132.4ºE 11.3ºS 59 ± 40 Southon et al., 2002

Port George, WA, Australia 125.25ºE 15ºS 194 ± 78 Bowman 1985

Broome, WA, Australia 122.23ºE 17.97ºS 112 ± 78 Bowman 1985

Broome, WA, Australia 122.23ºE 17.97ºS

-41 ±

119 Bowman 1985






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ΔR value of a given location accounts for deviations from the model 14

C age of the “global”

surface ocean (e.g., Marine09 curve; Reimer et al. 2009) owing to variations in local ocean

currents, ocean upwelling and in-flow of fresh water (Reimer & Reimer 2001, Ulm 2006).

Values of ΔR values have effectively been used to study ocean circulation and investigate

sources of regional sea water (Southon et al. 2002, Hua et al. 2004). Surface waters around

northern Australia are a mixture of the Indonesian Through-Flow (ITF) Current and a

seasonal southeast flow along the coast of Sumatra and local waters (Fig. 3.3).

FIG. 3.3. Oceanic circulation patterns in northern Australia and adjacent seas indicating directional flow of

major currents (adapted from Guilderson et al. 2009 and Hua et al. 2005). Dark arrows indicate direction

of flow during the southwest monsoon, light arrows indicate flow during the northeast monsoon and the

dashed arrows indicate year-round currents. ITF – Indonesian Through Flow. Numbers represent

local/regional ΔR values for northern Australia, Lombok and Mentawai. * indicate capture locations of

samples.


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In addition, there is a similarity between the average ΔR value for northern Australia of 63 ±

60 years (Fig 3.3), the average ΔR value for Lombok, Indonesia of 40 ± 30 years calculated

for the period 1938-1950 (Guilderson et al. 2009) and that for Mentawai, Indonesia of 82 ±

60 years calculated for 1944-1950 (Grumet et al. 2004). These suggest that sea waters around

Lombok and Mentawai Islands, Indonesia are similar to that of northern Australia in terms of

14C. Consequently,

14C concentrations of the birth bands from adult sharks sourced within

northern Australia were compared with the bomb curves from these regions to verify our age

estimates for birth bands of pigeye shark vertebrae.

EDGE ANALYSIS

Owing to the lack of shark vertebrae of known-age, annual deposition of growth increments

was also confirmed by edge analysis. Under this protocol, the characteristics of the final

growth increment prior to capture and subsequent death, i.e. translucent or opaque, was noted

for all individuals and plotted against catch date. Only 46 mature individuals were included

because they were all caught within the one month; juveniles less than one year old were

excluded due to the lack of clarity in band characteristics.

GROWTH MODELS

Once individual ages of sharks were estimated, five growth models were fitted to the length-

at-age data with R cran v2.11.1 (R Development Core Team 2010). These included the von

Bertalanffy growth function (VBGF) (von Bertalanffy 1938), the two-parameter von

Bertalanffy growth function (2VBGF; with fixed and variable birth size) (Fabens 1965, Hua

et al. 2004), the two-phase von Bertalanffy growth function (TPVBGF) (Soriano et al. 1992),


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the Gompertz growth function (GOMPERTZ) (Ricker 1975) and the two-parameter

Gompertz growth function (2GOMPERTZ; with fixed and variable birth size) (Mollet et al.

2002). Model parameters were estimated by least-squares nonlinear regression.

Size at birth was determined by the presence of open umbilical scars observed in

juvenile sharks in the current study and from published values; and was set at 650 mm for

pigeye and 640 mm for bull sharks. The effect of variation in birth sizes on parameter

estimates was determined by using Markov Chain Monte Carlo (MCMC) methods to

simulate growth models over a range of described birth sizes (650 – 750 mm for pigeye and

600 – 750 mm for bull sharks). Size at maturity (2100 – 2150 mm for male and female pigeye

sharks respectively and 2200 – 2300 mm for bull sharks) was taken from life history

characteristics described in Last and Stevens (2009) and correlated with the slowing growth

rate typical of carcharhinid sharks post-maturity. Age at maturity was calculated from

estimated age at size at maturity.

Akaike’s information criterion corrected for small sample size (AICc) was used to

assess model performance (Burnham & Anderson 2002). The bias-corrected relative weight

of evidence for each model, given the data and the suite of candidate models considered, was

determined by calculating its Akaike weights (ωi); the smaller the weight, the lower its

contribution to parameter estimates (Burnham & Anderson 2002). Parameter estimates were

then averaged by multiplying the growth coefficients by model ωi and summing estimates

over all suggested candidate models. A Student’s t test compared whether growth rates

between sexes were statistically different.


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RESULTS

AGE AND GROWTH ESTIMATES

Totals of 199 pigeye (89 females and 110 males) and 94 bull (47 females and 47 males)

sharks were aged. The total lengths for pigeye sharks ranged from 678 mm (both sexes) to a

maximum total length of 2550 mm for males and to 2560 mm for females. Few individuals of

sizes from 1200 – 2000 mm total length were collected (Fig. 3.4a).

FIG. 3.4a. Frequency distribution of total length (mm) size classes of pigeye shark (Carcharhinus

amboinensis; n = 199 (female n = 89, male n = 110); size class range from 600 – 2599 mm.


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Bull sharks ranged between 645 mm to maximum total lengths of 2760 mm for males, and

3180 mm for females. The majority (57%) of bull sharks sampled were between 645 to 1000

mm. There were no medium-sized individuals within the total length range of 1300 to 2200

mm (Fig. 3.4b).

FIG. 3.4b. Frequency distribution of total length (mm) size classes of bull shark (Carcharhinus leucas;

n = 94 (female n = 47, male n = 47); size class range from 600 to > 3000 mm).


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Within-reader precision for both species was high and between-reader variation was

low. Between-reader consensus age estimates were attained for all samples for both species;

consequently, all individuals were deemed readable. IAPE for individual vertebrae were

consistently less than 20 % (Wintner et al. 2002) (Fig. 3.5 and Fig. 3.6 for age-specific

within- and between-reader variability). Confidence levels were moderate (two) to high (three)

consistently across all total lengths for both species.

Individuals’ ≤ one year old were prevalent in both species (26.13 and 54.26 % of

pigeye and bull sharks, respectively). Most other age classes of pigeye sharks were also well-

represented, except ages 4 – 11 years and > 21 years, which only accounted for 10.5 and 1.5

% respectively of the total sample (combined sexes). The oldest male pigeye shark was 2370

mm in length and 21 years old, and the oldest female was 2490 mm in length and 24 years

old. There were four female pigeye sharks collected at size at maturity and these were

between 13 and 14 years of age. Similarly, there were four males at size at maturity, aged

between 12 and 14 years. Only 26 % of bull sharks were greater than 2000 mm in size;

consequently, most individuals (74.3 %) were juveniles four years and younger. The oldest

male bull shark was 2840 mm in length and 22 years of age and the oldest female was 3180

mm in length and 26 years of age. There was only one nine year-old female and no male bull

sharks at size at maturity.


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a)

b)

FIG. 3.5. Within principle reader variability represented by average percent error (APE as %) ± 95%

confidence interval for each consecutive number of growth increments. Sample sizes are given for each

corresponding band class. a) Pigeye shark (Carcharhinus amboinensis); b) Bull shark (Carcharhinus

leucas).


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a)

c)

FIG. 3.6. Comparison between mode principle and mean reader two-sigma (± 95 % confidence interval)

age estimates (years). Sample sizes are given for each corresponding age class. a) Pigeye shark

(Carcharhinus amboinensis); b) Bull shark (Carcharhinus leucas).


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VERIFICATION

Edge analysis

No opaque banding was found in the final growth increment that was deposited immediately

prior to capture during the months of March to August. The occurrence of opaque bands

increased after September, although we lacked samples from December to February (Fig. 3.7).

These results suggest annual band deposition.

FIG. 3.7. Frequency of final growth increment characteristics (translucent or opaque) per month for the

pigeye shark (Carcharhinus amboinensis). n = 99.

Radiocarbon analysis

Radiocarbon contents of five birth date bands agreed well with those from Mentawai and

Lombok bomb curves within 2σ uncertainties (Fig. 3.8). There is only one sample (OZL652 –

PE170) whose Δ14C level for the estimated birth year was higher than that recorded in

Mentawai but agreed with the value for Lombok within 2σ uncertainty.


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FIG. 3.8. Comparison of Δ14C values in birth bands of seven pigeye sharks, Carcharhinus amboinensis,

collected from the Timor Sea with those of the reference oceanic 14

C bomb curves from Mentawai (solid

black line) and Lombok (solid grey line), shown in 1-year running mean values. Dashed lines represent 2σ

uncertainties. The birth band samples are plotted against their estimated ages based on band counting.

Vertical and horizontal error bars, shown in 1σ, represent 14

C analytical and band counting uncertainties,

respectively.

GROWTH PARAMETER ESTIMATES

Growth parameters were averaged across all candidate models based on their Akaike weights

(ωi). The TPVBGF could not be fitted to either species because it over-parameterised the

VBGF. For female pigeye sharks, the GOMPERTZ function was the top-ranked growth

model according to AICc (Table 3.3; Fig. 6a). Incorporating variability in birth size only


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increased model weight in the 2GOMPERTZ. The weighted-average predicted values (and

S.E) of theoretical asymptotic lengths (L∞) (± S.E) and growth coefficient (k) were

2672 (± 11.94) mm and 0.145 year -1

, respectively.

TABLE 3.3. Information-theoretic growth model ranking for pigeye, Carcharhinus amboinensis

(males n = 110 and females n = 89) and bull, C leucas (sexes combined n = 94) sharks. AIC (Akaike’s

information criterion); Δ AICc (differences between model AIC values); ωi (AICc weights).

Model

AICc

ΔAICc

ωi

(a) pigeye shark

female male female male female male

TPVBGF - - - - - -

GOMPERTZ 1071.469 1324.503 0.000 1.371 0.871 0.334

2GOMPERTZ-variable 1075.527 1338.890 4.058 15.758 0.115 0.000

2GOMPERTZ-fixed 1079.751 1323.132 8.282 0.000 0.014 0.664

2VBGF-fixed 1088.065 1336.139 16.596 13.007 0.000 0.001

VBGF 1088.716 1337.244 17.247 14.112 0.000 0.001

2VBGF-variable 1098.372 1359.392 26.903 36.260 0.000 0.001

(b) bull shark

TPVBGF - - -

GOMPERTZ 1145.556 0.000 0.985

2GOMPERTZ-fixed 1154.599 9.043 0.010

2GOMPERTZ-variable 1157.606 12.050 0.002

2VBGF-fixed 1159.104 13.548 0.001

VBGF 1161.036 15.480 0.000

2VBGF-variable

1185.775

40.219

0.000

Model ranking criteria: TPVBGF (two-phase von Bertalanffy growth function); VBGF (von Bertalanffy Growth

Function); 2VBGF-fixed (two-parameter von Bertalanffy growth function fixed birth size); 2VBGF-variable

(two-parameter von Bertalanffy growth function with variable birth size); GOMPERTZ (Gompertz growth

function); 2GOMPERTZ-fixed (two-parameter Gompertz growth function fixed birth size); 2GOMPERTZ-

variable (two-parameter Gompertz growth function with variable birth size).


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Corresponding age at maturity was approximately 13 years and was estimated an

approximate maximum age (defined at 1 % of its asymptotic length) at > 30 years (Fabens

1965) (Table 3.4). Juvenile growth rate was 151 mm year-1

for the first 5 years, slowing to

< 25 mm year -1

post-maturity.

For male pigeye sharks, the 2GOMPERTZ function was top-ranked (Table 3.3; Fig.

3.9a) although both the 2GOMPERTZ and GOMPERTZ had similar AICc values. As for

females, incorporating variability in birth size only reduced model ranking in

2GOMPOERTZ (Table 3.4). Model-averaged growth parameters were 2540 (± 13.05) mm

and 0.161 year -1

for the theoretical asymptotic length (L∞) (± S.E) and growth coefficient (k),

respectively. Age at maturity of males was similar to females, with males maturing slightly

earlier at approximately 12 years. Maximum age was > 26 years. Juvenile growth rate

resembled that of females and was estimated as 147 mm year -1

for the first five years,

slowing to < 20 mm year -1

post-maturity. Growth rates were not significantly different from

female conspecifics (t179.8 = -1.614, p = 0.108).

We did not estimate sex-specific growth parameters for bull sharks due to the lack of

large individuals; rather, growth was determined for both sexes combined (Table 3.4; Fig.

3.9b). The GOMPERTZ function was top-ranked model for this dataset (Table 3.3).

Comparison between model-specific growth parameters and model-averaged estimates

displayed similar patterns, as for pigeye sharks (Table 3.4). Model-averaged growth

parameters for these length-at-age data are theoretical asymptotic length

(L∞) (± S.E) = 3119 (± 9.80) mm and a growth coefficient (k) = 0.158 year -1

respectively.

Age at maturity was 9.5 years and like pigeye sharks, bull sharks are long-lived with a

predicted longevity of > 27 years (Table 3.4). Juvenile growth rate was 186 mm year -1

for

the first five years and then slowed to 45 mm year -1

post-maturity.


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TABLE 3.4. Age and growth parameter estimates for a) pigeye, Carcharhinus amboinensis (males n = 110;

females n = 89) and b) bull, C. leucas (sexes combined n = 94) sharks. L∞ , theoretical asymptotic length; k,

growth rate; t0, age when the individual would be zero length 1

.

Model

L∞ (± standard

error) (mm)

max age

(years)

k (year -1

)

t0

age at

maturity

(years)

(a) pigeye shark

female

male

female

male

female

male

female

male

female

male TPVBGF - - - - - - - - - -

GOMPERTZ 2668

(45.66)

2550

(53.20)

> 30 > 26 0.146 0.158 1.944 1.863 13 12

2GOMPERTZ -

variable

2710

(48.63)

2665

(68.41)

> 31 > 29 0.136 0.133 - - 13 13

2GOMPTERTZ - fixed

2620 (38.20)

2529 (42.33)

> 30 > 27 0.160 0.163 - - 12.5 12

2VBGF - fixed 2855

(75.45)

2847

(99.12)

> 38 > 36 0.090 0.085 - - 13 12.5

VBGF 2895

(92.28)

2794

(106.92)

> 38 > 33 0.085 0.091 -3.125 -2.794 13 12

2VBGF - variable 3035 (116.4)

3135 (188) > 41 > 39 0.072 0.065 - - 12 13

Model averaged 2672

(11.94)

2540 (13.06 > 30 > 26 0.145 0.161 - - 13 12

(b) bull shark

TPVBGF - - - - -

GOMPERTZ 3120 (65.65) > 27 0.158 2.601 9.5 2GOMPERTZ - fixed 3025 (50.25) > 25 0.179 - 9

2GOMPTERTZ -

variable

3241 (78.44) > 30 0.137 - 9.5

2VBGF - fixed 3481 (115) > 38 0.084 - 9.5

VBGF 3507 (143.3) > 37 0.082 -2.485 9.5

2VBGF - variable 3979 (249.3) > 43 0.059 - 10

Model averaged 3119 (9.80) > 27 0.158 - 9.5

1 Model abbreviations as in Table 3.3


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(a)

(b)

FIG. 3.9. Growth models fitted to total length (mm) - at-age (years) data. Solid line: von Bertalanffy

Growth Function (VBGF), dashes: two parameter von Bertalanffy growth function (2VBGF), dotted:

gompertz growth function (GOMPERTZ), dot-dash: two parameter gompertz growth function

(2GOMPERTZ). Growth functions do not incorporate variation in birth size. (a) Pigeye shark

(Carcharhinus amboinensis); (b) Bull shark (Carcharhinus leucas).


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DISCUSSION

AGE AND GROWTH PARAMETERS OF BULL AND PIGEYE SHARKS

We have provided the first estimates of age and growth for the pigeye shark despite its global

distribution in tropical and subtropical waters and its targeted and passive take as bycatch by

Australian commercial fisheries. The age and growth estimates show the K-selected life

history traits (slow growth, late maturity and long-lived) typical of other large carcharhinids

and indicative of high susceptibility to over-exploitation (Campana et al. 2002,

Simpfendorfer et al. 2002, Field et al. 2009a). Management strategies regulating acceptable

mortality due to fishing must reflect these pressures.

The notoriety of the bull shark as one of the most aggressive sharks in the world and

the high frequency of human interactions with this species has increased public awareness of

it and the pressure to quantify species resilience. For this reason, multiple age and growth

studies are available from different locations within its distribution (Branstetter & Stiles 1987,

Wintner et al. 2002, Cruz-Martinez et al. 2004, Neer et al. 2005). Our estimates of age and

growth parameters confirm that bull sharks in northern Australia obtain greater total lengths

than conspecifics from other locations, and like pigeye sharks, they have also evolved slow

vital rates.

Regional differences in age and growth parameters have been used to distinguish

population structure in other carcharhinds, suggesting Australian bull sharks have diverged

from other populations (Carlson & Baremore 2005, Carlson et al. 2006, Romine et al. 2006).

Current estimates of juvenile growth rates within northern Australia are similar to estimates

by Thorburn & Rowland (2008) and suggest that juvenile bull sharks have similar growth

rates to those estimated from other populations (Branstetter & Stiles 1987). However, as they

mature, Australian bull sharks appear to grow at a high rate similar to those in South African


Page | 42

waters, but mature earlier than individuals from the Gulf of Mexico (Branstetter & Stiles

1987, Wintner et al. 2002, Cruz-Martinez et al. 2004). Despite Australian bull sharks

obtaining larger sizes than in other locations, individuals from all populations appear to be

similarly equally long-lived (Branstetter & Stiles 1987, Wintner et al. 2002, Neer et al. 2005).

Our growth parameter estimates for both species are biologically realistic but slightly

underestimate maximum total length (Compagno 1984, Last & Stevens 2009). This might

arise because few very large and old individuals were collected by our study. The lack of sex-

specific variation in parameter estimates in the pigeye shark also seems reasonable, as these

do not differ between sexes in other large carcharhinids (Branstetter & Musick 1994, Joung et

al. 2008). Restricted sample sizes prevented statistical testing of the effects of gender on

growth of Australian bull sharks, although the differences we noted in maximum sizes

between sexes hint at this possibility. It is possible that the similar growth rates between

species in our study, despite bull sharks maturing larger and obtaining greater total lengths

than pigeye sharks, reflects the counteraction of sex-specific age and growth trends. Not all

bull shark populations exhibit these sex differences (Wintner et al. 2002, Neer et al. 2005),

and the presence of this trend might again support the divergence of Australian populations

from those of other regions. The lack of medium-sized individuals in this study most likely

reflects age-based habitat partitioning similar to South Florida (Simpfendorfer et al. 2005,

Yeiser et al. 2008, Heupel et al. 2010b).

VERIFICATION OF ANNUAL BAND DEPOSITION IN THE PIGEYE SHARK

Annual band deposition was supported by both independent methods (edge analysis and

bomb-radiocarbon dating). Unfortunately, intense tropical weather prevented acquisition of

samples from December to February for edge analysis. The lack of opaque banding between


Page | 43

March and August and its subsequent increase in prevalence after September strongly

suggests annual deposition of growth increments in pigeye sharks.

Our study is the first to apply bomb radiocarbon dating to corroborate age estimates of

large sharks from the Indo-Pacific region. Campana et al. (2002) confirmed that 14

C

concentrations are temporally stable with no leaching of 14

C into consecutive growth bands

post-deposition or metabolic reworking of vertebral material. Consequently, the quantified

vertebral 14

C concentrations we measured were real and representative of the environments

inhabited while increments were being deposited. The good agreement between Δ14C values

calculated based on estimated shark birth dates and those of the reference oceanic 14

C bomb

curves from Mentawai and Lombok (Indonesia) confirms that our age estimates are likely to

be realistic in most cases and is further evidence that band deposition is likely to be annual, as

occurs in other carcharhinids. The lack of correlation between 14

C concentration within one

individual and the Mentawai reference bomb curve might represent a different birth location

and subsequent migration to the capture location, which is typical of large marine predators.

Unfortunately, we were unable to obtain vertebrae from known-age sharks born

during the 1960s to 1970s correlating with the region of the bomb curve with the greatest

change in atmospheric 14

C providing the highest resolution on the bomb curve. Furthermore,

the recent development of this application as a tool for ageing marine fauna in this region

compared with other studies i.e. the Atlantic, limited comparative bomb curves to carbonates,

such as coral, rather than other sharks or teleost fishes that have similar mineralisation

processes to those of our study species (Campana et al. 2002, Cruz-Martinez et al. 2004,

Ardizzone et al. 2006, Francis et al. 2007). The use of shallow inshore nurseries by juveniles

supports the assumption that these sharks are not embarking on seasonal migrations and

remained in shallow coastal waters when the birth band was deposited. This method also

assumes that juveniles are not moving far from nursery grounds as adults, which is supported


Page | 44

by isotopic and genetic analyses (Tillett et al. 2011b, Tillett et al. 2012a). Despite the

limitations of this method, analysis of bomb radiocarbon enabled a definitive classification of

broad-scale trends, such as K-selected life history traits, which have important implications

for fisheries management and our understanding of species’ ecological roles.

INFLUENCE OF BIRTH-SIZE VARIABILITY ON PARAMETER ESTIMATES

Incorporating variability in birth size weakened the performance of two-parameter growth

functions in all length-at-age data sets, except data for female pigeye sharks. Inclusion of

variability in this trait within candidate models increases the flexibility within the shape of

that function and, as a consequence, models become more susceptible to overestimating

longevity should the dataset lack larger, older individuals, as was typically the case in the

present study. Only the data sets of length-at-age of female pigeye sharks attained a natural

asymptote, and thus did not require a model to predict this plateau. For these sharks, the

addition of variability in birth size produced more biologically realistic parameter estimates

that were nearer to recorded maximum total lengths for both species within Australia. Neer et

al. (2005) also concluded that including variability in birth size produced more biologically

realistic estimates of growth parameters for bull sharks in the Gulf of Mexico, although this

study only fitted the von Bertalanffy growth function and its derivatives to data sets.

ACCURACY AND PRECISION OF PARAMETER ESTIMATES

Our metrics of precision (index of average percent error and coefficient of variation) were

within the lower range of estimated error rates for carcharhinids (Santana & Lessa 2004,

Carlson & Baremore 2005). The predominance of juvenile bull sharks aged ≤ 1 year in our


Page | 45

samples might have downwardly biased our estimated error, although no error for individual

vertebra exceed 20 % . Consistent with age studies on other sharks, within-reader variation

for bull and pigeye sharks increased with increasing total length, reflecting the compression

of annuli as growth rate slows in older individuals (Officer et al. 1996). The challenge of

ageing larger, older individuals may be further complicated by the stability of the tropical

environment from which the sharks were sampled (Lessa et al. 2006).

Low numbers of individuals at maturity, particularly for bull sharks, might have

influenced the inflexion point of the growth models. This could have caused an

underestimation of age at maturity and an overestimation of the growth coefficient.

Additionally, the lack of large, older individuals could be responsible for the relatively poor

performance of the von Bertalanffy growth models and its derivatives. No asymptote was

attained by these models over the examined age range, potentially causing an overestimation

of the theoretical asymptotic length and underestimating the growth coefficient. The S–

shaped logistic curve of the Gompertz growth models forces the relationship to an asymptote,

thus providing more biologically-realistic growth estimates. The predominance of juveniles ≤

1 year provided a non-variable base to the functions, commonly lacking in other studies, and

the difference in longevity estimates arises depending on how candidate models project

growth in the older individuals that were lacking in our study and would have removed

variability in that portion of the function .

Our results reinforce the notion that these two species of apex predator are highly

susceptible to over-exploitation. The lack of larger individuals of both species needs to be

further investigated, with changes in policy effected to reduce the fishery mortality of these

larger size classes and those near maturity, to ensure the species’ reproductive potential is not

reduced. Additionally, the ecological diversity within the genus Carcharhinus requires

region- and species-specific understanding of these predators’ functional roles in the


Page | 46

ecosystem. Similar size does not necessarily imply analogous function, although in the

present study it does imply similar resilience and susceptibility to over-exploitation. Despite

the larger total length obtained by bull sharks, its demographic similarity with pig-sharks

suggests that it is just as vulnerable to over-exploitation and as such, its IUCN Red List

classification should be upgraded to Near Threatened, as it is for bull sharks. Furthermore,

this study reiterates the need for careful management of marine resources across northern

Australia, if this region is to remain one of the last strong-holds for the marine megafauna of

tropical South East Asia (Field et al. 2009a).

ACKNOWLEDGEMENTS

This study was funded by the Tropical Rivers and Coastal Knowledge Research Hub, Charles

Darwin University, Darwin, the Australian Institute of Marine Science and the Australian

Institute of Nuclear Science and Engineering (AINSE). Sampling was undertaken with the

kind support of fishermen, Wildlife Resources Inc, Kakadu National Park and the Department

of Resources – Fisheries, Northern Territory. K. Strobel, Charles Darwin University assisted

with shark ageing. Samples were collected under the S17 fisheries permit number 27134 and

Kakadu permit number RK 689. Research was in agreement with animal ethics clearance

number A07001.

CHAPTER FOUR – Decoding fingerprints:

elemental composition of vertebrae correlate to age-

related habitat use in two morphologically similar

sharks.

Published: Tillett,B.J., Meekan, M.G., Parry, D., Munksgaard, N., Field, I.C., Thorburn, D. and

Bradshaw, C.J.A. (2011). Decoding fingerprints: elemental composition of vertebrae correlate to age-

related habitat use in two morphologically similar sharks. Marine Ecology Progress Series. 434:133-

142. DOI:10.3354/meps09222.

Chapter 4– Inter and intra specific resource (habitat) partitioning

Page | 47

INTRODUCTION

The persistence of sharks as apex predators in marine environments is threatened by over-

exploitation and habitat change (Myers et al. 2007, Field et al. 2009b). Their k-selected life

history means that they have reduced resilience to rapid change and are likely to be slow to

recover from population decline (Schindler et al. 2002). Effective management and

conservation of these predators is hindered by poor knowledge of movement patterns (Speed

et al. 2010). This information is essential because it can identify key habitats (e.g., pupping

areas, nurseries etc) within distribution ranges and helps define the ecological role of species.

Additionally, it can identify evolved behaviours such as natal and pupping site fidelity (sensu

Speed et al. 2010) that are critical to the maintenance of genetic diversity and replenishment

of populations (Hueter et al. 2005).

Studies tracking shark movements and identifying patterns of habitat use in coastal

regions typically involve tagging with standard (numerical), satellite or acoustic tags (Speed

et al. 2010). Such an approach is often logistically difficult and expensive because it first

involves the capture, tagging and release (in good condition) of the shark. Furthermore, the

animals must either be recaptured (standard tags), or tags must report to satellites or arrays of

listening stations (acoustic tags) for data acquisition (Voegeli et al. 2001, Simpfendorfer &

Heupel 2004). Rates of recapture are usually low, while failure of expensive satellite tags to

report is commonplace (Hays et al. 2007). Arrays of listening stations require considerable

effort to deploy, download and maintain, which can limit the duration and spatial extent of a

study using this approach. Despite these problems, studies using these techniques have

mapped fine-scale (25 km) movements of different-age cohorts of sharks in shallow coastal

waters (Simpfendorfer et al. 2005, Heupel & Simpfendorfer 2008, Yeiser et al. 2008,

Heithaus et al. 2009, Ortega et al. 2009, Heupel et al. 2010b), but the logistics, cost and


Page | 48

limited life span of tags have restricted the number of target individuals and species and in

the case of acoustic tags, the spatial extent of the sampling area.

To overcome the limitations associated with conventional tracking, natural chemical

fingerprints are a developing tool to trace age-related movements of sharks among habitats

throughout their lifetime. This method is similar to tracking fish movements based on otolith

microchemistry. Environmental signatures (‘fingerprints’) stem from either pollution or

natural leaching and weathering of elements from the Earth’s crust into aquatic systems

(Campana 1999, Gillanders 2005, Speed et al. 2010). As individuals living and growing in

these environments osmoregulate, trace elements are absorbed across the skin and gills and

can either be substituted for calcium or trapped within protein matrices of hard structures

such as otoliths or vertebrae (Gillanders & Kingsford 2000, Dean & Summers 2006, Hale et

al. 2006). In otoliths, elements are deposited in concentrations reflecting that of the aquatic

environment and can be influenced by the physical properties of that medium (Gillanders &

Kingsford 2000, Walther & Thorrold 2006, Brown & Severin 2009). For example, strontium

(Sr) typically has relatively high concentrations and is uniform in marine environments, while

barium (Ba) shows the opposite pattern and is enriched in freshwater or during flood periods

in the low salinity region of freshwater plumes (McCulloch et al. 2005, Crook & Gillanders

2006). Concentrations of elements in marine environments reflects proximity to freshwater

inputs or nutrient upwellings (Beamish et al. 2005, Kingsford et al. 2009). Correlating

mineralisation with growth increments within these structures enables age-based

interpretation of records that can be used to describe the periodicity of habitat use (Gillanders

& Kingsford 2000, McCulloch et al. 2005).

We apply similar principles as a baseline approach to investigate whether changes in

vertebral microchemistry correlate with known age-specific changes in habitat use of bull

(Carcharhinus leucas) and pigeye (C. amboinensis) sharks. Both of these species are large


Page | 49

high-order predators (3400 and 2800 mm maximum total length in Australia, respectively)

within shallow waters of tropical and subtropical coasts (Last & Stevens 2009). Pop-up

satellite tags have confirmed the affinity of large bull sharks to shallow coastal environments

and tracked a few individuals embarking on long-distance movements (1506 km)

(Brunnschweiler et al. 2010, Carlson et al. 2010). Bull sharks use freshwater nurseries with

juveniles remaining in these areas for approximately 4 years (Thorburn & Rowland 2008,

Heupel et al. 2010b, Curtis et al. 2011, Heupel & Simpfendorfer 2011). Preliminary genetic

assessment suggests female pupping site fidelity, although it is unclear how often females are

returning to pup and whether males also return, which might imply that mating as well as

pupping occurs in freshwater (Tillett et al. 2012b). Short-term tracking research (~ 1.5 years)

has suggested that broad-patterns of habitat use correlate with the onset of maturity

(Simpfendorfer et al. 2005, Heupel & Simpfendorfer 2008, Yeiser et al. 2008, Heupel et al.

2010b). Conversely, adult pigeye sharks do not show this pattern; rather, population genetic

structure suggests restricted movement (Tillett et al. 2012a). Acoustic tracking studies

indicate age-based partitioning of habitat with juveniles of this species occupying areas

adjacent to creek and river mouths (Last & Stevens 2009, Knip et al. 2011a).

We hypothesise that periodic chemical signatures of freshwater or low-salinity

environments (indicated by low Sr:Ba ratios for bull and declines in element:Ca ratios for

pigeye sharks) will occur in vertebrae of adults, indicating returns to nurseries. These should

be more evident in females because they return regularly to pup in freshwater or estuarine

nurseries. If there is natal site fidelity occurring in these sharks, the chemical signatures from

adult birth bands should be similar to those deposited in the vertebrae when they return to the

same freshwater nursery to pup. Second, we hypothesise that chemical signatures will change

post-maturity coinciding with changes in habitat use. Third, Sr:Ba ratios should differ in

young juvenile stages ( 150 mm) of bull and pigeye sharks because the former use


Page | 50

freshwater nurseries while the latter use nurseries in shallow coastal areas. As sharks grow

and leave these nurseries, ratios should be increasingly similar between the species because

of greater habitat overlap. Ultimately, post-maturity ratios should also be similar because

both species are found in deeper coastal water (approximately 20 m) as adults.


VERTEBRAE COLLECTION

We removed 10 to 15 thoracic vertebrae from 88 (39 adults, 49 juveniles) pigeye and 92 (18

adults and 74 juveniles) bull sharks. Adult sharks were collected by scientific observers

working with the Northern Shark Fishery operating along the Northern Territory coastline in

2009. Long-lines must not exceed 15 nautical miles with no more than 1000 snoods (hooks),

and nets must be 1000 – 2500 m long with a square mesh size of 150 – 250 mm and a drop of

50 – 100 meshes. We obtained juvenile sharks from both commercial fisheries and our field

work from 2002 – 2009. Fishery-independent studies were done using long-lines

approximately 50 m long with 50 snoods (hook size 11/0) positioned 1 m apart, and nets

approximately 50 m long with a square mesh size of 150 - 250 mm with a 16-mesh drop.

Both were weighted and deployed along the bottom in depths ranging from 5 – 15 m. We

collected juvenile pigeye sharks from inshore coastal waters around Broome, Western

Australia, from the north-eastern side of the Joseph Bonaparte Gulf to the Gulf of Carpentaria,

Northern Territory and Townsville, Queensland (Fig. 4.1).


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FIG. 4.1. Capture locations for the pigeye Carcharhinus amboinensis (total n = 88) (39 adults, 49

juveniles) and bull Carcharhinus leucas (total n = 92) (18 adults and 74 juveniles) sharks across northern

Australia.

We collected juvenile bull sharks from six different northern Australian river systems. These

included the Liverpool, Roper, Towns, Fitzroy, Daly and East Alligator Rivers (Fig. 4.1).

Sample sizes, sex ratio and capture dates from each location are provided below (Table 4.1).


Page | 52

TABLE 4.1. Sampling details for fishery independent survey’s (species combined). Location, sample size

(sex ratio), species, average total length (TL) ± standard deviation (mm), date (months / years).

Location

Sample size (sex

ratio)

Species

Average TL (± st

dev) (mm)

Date

Darwin

harbour 4 (2 m, 2 f) C. amboinensis 753.75 (43.28) March 2008

Fitzroy R 16 (8 m, 8 f) C. leucas 994.18 (295.36) June 2003

Daly R 15 (9 m, 6 f) C. leucas 862.53 (99.77) July / August 2007

Liverpool R 7 (5 m, 2 f) C. leucas 904 (96.87) June 2002

East

Alligator R 14 (7 m, 7 f) C. leucas 767.62 (26.87) April 2008

Roper R 11 (5 m, 6 f) C. leucas 836.18 (47.5) July 2002

Towns R 11 (5 m, 6 f) C. leucas 1092.92 (130.85) July 2002

We measured sex, total weight (TW) total length (TL) and fork length (FL) when possible.

We inspected small individuals (< 1 m TL) for the presence of umbilical scars as an

indication of time since birth. Once thoracic vertebrae were removed, we stored them frozen

or temporarily immersed them in 5 % sodium hypochlorite solution before storing dry. Due

to the morphological similarities between members of the genus Carcharhinus, we collected

a small tissue fragment (~ 1 g) from each individual and genetically tested it to confirm

species identification (Tillett et al. 2012a, Tillett et al. 2012b).

PREPARATION OF VERTEBRAE

We defrosted frozen samples and excised excess tissue, neural and haemal arches to expose

the centra. We separated individual centra and removed any connective tissue with a scalpel

blade or abrasive material. We subsequently washed centra in Milli-Q water and left polished

centra to air-dry in a fumehood causing any remaining tissue to become brittle and peel away.

We immersed remaining vertebrae (74 juvenile bull and 16 juvenile pigeye sharks) in 5%

sodium hypochlorite solution for approximately one minute or until remaining flesh had been


Page | 53

removed. Again, we excised neural and haemal arches to expose the centra. We weighed all

cleaned vertebrae and embedded them whole in a two-part casting-laminating epoxy resin

(Barnes, New South Wales, Australia), through which we sectioned using a low-speed isomet

diamond saw at approximately 240 RPM with a 250 g load weight. We ground sections on

wet and dry paper until ~ 0.5 mm thick and rinsed again in Milli-Q water to remove potential

contaminants (i.e., the outer edge potentially containing absorbed resin was removed). We

mounted sections on glass slides using either Crystalbondtm

509 (ProSciTech, Queensland,

Australia) or with a temporary adhesive (Blu Tac, Bostik) and took care not to contaminate

vertebral regions to be ablated. We viewed sections under a Leica DM 400B compound

microscope and marked the desired starting position for each analysis by a small incision in

the surrounding resin.

AGEING

We viewed vertebral sections using the imaging software package OPTIMAS 6.5 (Media

Cybernetics L. P., Silver Spring, USA) and aged by counting growth bands (defined as one

opaque and one translucent ring) visible along the corpus calcareum reading from the focus to

the outer centrum edge (Fig. 4.2) (Campana 2001). We regarded the change of angle caused

by differences in growth rate from intra-uterine to post-natal life history stages as the point of

birth, or birth mark and recorded it as year zero (Cailliet & Goldman 2004). Annual growth

band deposition has been confirmed for bull sharks (Branstetter & Stiles 1987, Neer et al.

2005) and pigeye sharks (Tillett et al. 2011a).


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FIG. 4. 2. Sagittal section of bull shark vertebrae (aged 10 years) displaying annual growth increments,

and descriptions of vertebral attributes.

PRELIMINARY STUDY

Initial analyses determined (1) the concentric manner in which the calcareous vertebral

matrix was deposited and the corresponding 3-dimensional structure of the sagittal section, (2)

inter-vertebral variation in chemical composition of the matrix, and (3) if temporary

bleaching to remove excess tissue resulted in the leaching of elements from the vertebral

matrix. For this analysis, we removed four thoracic vertebrae from two individuals of each

species. After air drying, we removed excess connective tissue by trimming (two vertebrae)

or bleaching (two vertebrae). We then set all four vertebrae in resin, sectioned and mounted

Summer

band

Winter

band

Birth

band

Intermedalia

Total

radiusCorpus

calcareum

Focus


Page | 55

them on slides as described above. We analysed chemical compositions to compare effects of

treatments.

LASER ABLATION-INDUCTIVELY COUPLED PLASMA-MASS SPECTROMETRY

We analysed samples using a 213-nm laser (Nd:YAG, 5th

harmonic; NewWave Research

UP-213) coupled to an ICP-MS (Agilent 7500ce) connected to a Hitachi camera (Charles

Darwin University, Darwin, Northern Territory, Australia). The main operating parameters

are shown in Table 4.2. We optimised the LA-ICP-MS for maximum sensitivity by adjusting

He and Ar flows and plasma power during ablation of the National Institute of Standards

(NIST) 612 glass standard. We monitored oxide formation by the ThO+:Th

+ ratio which was

typically < 0.5 %. We monitored instrumental drift by ablating the NIST 612 glass

standard after ablating 7 shark samples. If variation between NIST 612 glass standard was

greater than 5 %, we retested shark samples.

TABLE 4.2 Operating parameters for LA- ICP-MS (Agilent 7500ce).

Instrument 213 nm/Agilent 7500Ce

(Charles Darwin University)

Analysis type Spot Linescan

Spot size [m] 110 110

Pulse energy at sample [mJ] 0.9 0.9

Energy density at sample [J cm -2

] 10 10

Pulse frequency [Hz] 5 5

Linescan speed [m s -1

] - 15

Ablation cell He flow [L min-1

] 1.1 1.1

ICP_MS nebulizer Ar flow[L min -1

] 1 1

ICP-MS-RF power [W] 1200 1200

ICP-MS dwell times [ms] 30 30

ICP-MS replication time [s] 7Li,

25Mg,

55Mn,

66Zn,

86Sr,

137Ba

= 30

7Li,

25Mg,

55Mn,

66Zn,

86Sr,

137Ba

= 30

Total analysis time (blank/sample) [s] Spot: 20 / 60 20 / *

* dependent on the length of the scan, maximum length 2 mm


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Prior to activation of the laser, we recorded elemental composition of the blank

sample gas for 20 seconds. We made initial measurements of 13 elements (7Li,

25Mg,

27Al,

31 P,

43Ca,

55Mn,

54Fe,

63Cu,

64Zn,

86Sr,

139La,

137Ba

and

238U) that vary in concentration in

otoliths among different aquatic environments, but are not linked to diet (Milton & Chenery

2001, Cailliet & Goldman 2004, Kraus & Secor 2004, Martin & Thorrold 2005, Brown &

Severin 2009). We can only assume that these same elements are equally unaffected by diet

when isolated from vertebral tissues. From this list, we omitted from further analysis those

elements that were not recorded consistently above detection limits (calculated as three times

the standard deviation of the blank signal). Elements (7Li,

25Mg,

55Mn,

86Sr,

64Zn and

137Ba)

were all well above detection limits calculated using the LA-ICPMS software Glitter (Van

Achterbergh et al. 2001) (0.006, 0.069, 0.070, 0.318, 0.061, 0.013 μg g-1

, respectively).

For both species, we quantified elemental signatures of nursery areas by spot analyses

of the later region of the first six-month period (typically translucent) of the birth band (see

above, Fig. 4.2) of the sectioned vertebrae of juveniles (up to 850 mm total length, Last &

Stevens 2009). We investigated age-related habitat use and the return to natal environments

by ablating along the growth axis of the corpus calcareum (referred to as ‘line scans’) within

the vertebrae initiating from the birth band, moving across annual growth increments towards

the distal edge of the section (Fig. 4.2) on mature individuals (> 2200 mm total length). We

pre-ablated line scans to remove potential surface contaminants.


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ANALYSIS

Scanning electron microscopy (SEM) with X-ray Energy Dispersive Spectrometry (EDS) of

two vertebral sections from each shark species confirmed constant percent composition of Ca

(35 %) throughout the vertebrae, allowing use of this element as an internal reference

standard (Fig. 4.3) in LA-ICP-MS.

FIG. 4.3. Scanning electron microscopy of sagitally sectioned ablated shark vertebrae. Boxes represent

areas in the corpus calcareum which elemental compostion were quantified and compared (1 mm x 1 mm).

a) sample PE168 (Carcharhinus amboinensis) b) sample PE169 (Carcharhinus amboinesis) c) sample

B201 (Carcharhinus leucas) c) sample B229 (Carcharhinus leucas).


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This, combined with the reference silicate glass (NIST-612; National Institute of Standards

and Technology, Gaithersbug,MD) as an external calibration standard within each analytical

session, enabled the conversion of elemental count rates into concentrations (ppm) using the

data-processing software Glitter (Van Achterbergh et al. 2001). National Institute of

Standards and Technology (NSIT) glasses have been used as a calibration standard for the

analysis of carbonates and calcified teleost otoliths (Munksgaard et al. 2004, Hale et al. 2006,

Steer et al. 2009). Glitter’s data reduction accommodates gas blank corrections. The vertical

homogeneity (signature does not change with depth through the section) of the sections

enabled the user-selectable ‘quantitation’ window to quantify only the part of the analysis

where the analyte signals were stable, thus eliminating potential contaminants ablated with

surface material. We checked all analytical spectra for non-target signals represented by the

rapid decline in 43

Ca count rate. The variation in analyte concentrations of the NSIT 612

standards remained within acceptable limits (± 5 %) within each analytical session.

We compared the chemical signature for each nursery using Euclidean-distance

similarity matrices of standardised elemental concentrations. We evaluated the evidence for

differences between signatures using analysis of similarity (ANOSIM) which uses

permutations to determine if the assigned groups are more similar in composition than

samples from other groups (Chapman & Underwood 1999, Clarke & Gorley 2001). ANOSIM

generates an R statistic which indicates the magnitude of difference among groups and ranges

from -1 to 1. Values of ‘0’ represent no difference between groups and ‘1’ indicates that

groups differ completely. Statistical evidence for differences due to unique environmental

signatures is determined by comparing the sample R grouped by nurseries with those

produced by randomly assigning samples to groups. The portion of random arrangements

with R values greater than the sample gives the probability of observed patterns arising at


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random. Similarity percentages (SIMPER) then determined which elements differed between

nursery signatures by calculating the average distance (based on Euclidean-distance) within

and between all nurseries estimating the percent contribution of each element to the overall

distance (PRIMER-E, PML Plymouth, U.K). We screened data for the presence of outliers

(based on Mahalanobis distances of the observations) and conformity with multivariate

normality (Kolmogorov-Smirnov test), and removed those identified.

Analysis of line scans using Glitter provides an average composition of the selected

scan (or portion of scan), but does not quantify spatial variation within the selected scan.

Therefore, we transformed the analyte count data from each ICP-MS replicate into net count

rates relative to net count rates for the internal standard (Ca) and transposed these ratios as a

function of the ablated line scan distance using a customised ExcelTM

spreadsheet. We

smoothed the data using a running median and average calculation of 8 ICP-MS replicates

(equivalent to a scan distance of 35 m) (Munksgaard et al. 2004). We correlated chemical

signatures with growth increments to identify age-specific movement patterns. We

characterised periodic movements of adults into nurseries by declines in Sr:Ba ratios

(indicating time spent in marine/freshwater habitats for bull sharks) and reductions in

element:Ca ratios (indicating time spent in offshore/onshore movement for pigeye sharks)

(McCulloch et al. 2005, Clarke et al. 2009). We used Glitter’s ‘quantitation’ window to select

birth bands and sections in the line scan analysis thought to indicate movements into nursery

environments. We compared similarity in multi-elemental composition between these returns

and adult juvenile stages with nurseries identified from juvenile sharks using ANOSIM.


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RESULTS

Elemental composition was consistent between spot analyses of same age sections of the

corpus calcareum within vertebrae confirming the concentric growth of vertebrae

(Global R = 0.095, p = 0.251, Global R = 0.067, p = 0.345 for pigeye and bull sharks,

respectively). Similarly, elemental composition was consistent between spot analysis of the

same age sections of the corpus calcareum between vertebrae, confirming inter-vertebral

mineralisation was also constant (Global R = 0.058, p = 0.222; Global R = 0.086, p = 0.191

for pigeye and bull sharks, respectively). Bleaching vertebrae to remove connective tissue did

not cause leaching of elements from the vertebral matrix, supported by similar elemental

composition of spot analysis between same-age sections of vertebrae treated differently to

remove connective tissue (Global R = 0.039, p = 0.296; Global R = 0.009, p = 0.378 for

pigeye and bull sharks, respectively).

SIMPER (similarity percentages) of spot analyses on juvenile vertebrae quantified the

percent contribution of each element to nursery signatures. The contribution of each element

to these signatures differed between nurseries (Fig. 4.4).


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FIG. 4.4. Similarity Percentages (SIMPER) – element contributions to bull shark nursery signatures. Total

n = 74, (Fitzroy River n = 16, Daly River n = 15, Liverpool River n = 7, East Alligator River n = 14, Roper

River n = 11, Towns River n = 11).

Barium (137

Ba) was characteristic in the Liverpool River and almost absent from the East

Alligator and Towns Rivers signatures. Lithium (7Li) and Strontium (

86Sr) contributed

similarity to signatures in all locations except the Liverpool River. ANOSIM (analysis of

similarity) confirmed differences (indicated by Euclidean distances) between nurseries were

due environmental signatures rather than randomly generated (Overall global R = 0.373,

p = 0.0001; Table 4.3).


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TABLE 4.3. Pairwise ANOSIM correlations among Carcharhinus leucas nurseries indicating

whether differences are due to distinct environmental signatures or random factors *.

Fitzroy Daly Liverpool E. Alligator Roper Towns

(n = 16) (n = 15) (n = 7) (n = 14) (n = 11) (n = 11)

Fitzroy - 0.374 0.306 0.296 0.191 0.358

Daly 0.0001 - 0.61 0.475 0.252 0.389

Liverpool 0.0110 0.0001 - 0.529 0.58 0.495

East

Alligator 0.0003 0.0001 0.0003 - 0.438 0.473

Roper 0.0090 0.0030 0.0002 0.0001 - 0.528

Towns 0.0007 0.0003 0.0007 0.0001 0.0001 -

*R values ranging from ~ 0 (no different) to 1 (highly different) are above diagonal; probability of differences

arising purely at random as opposed to unique environmental signatures are below the diagonal.

Bull sharks displayed large shifts in vertebral microchemistry with age (Fig. 4.5).

Juvenile Sr:Ba net counts per second (cps) ratios differed among individuals and ranged

between < 100 – 300. Ratios either remained constant or steadily increased (to 300 – 700)

until maturity (8 – 10 years). Mature females (n = 14) showed cyclic declines in Sr:Ba ratios

(every one to two years) (Fig. 4.5a). Cyclic patterns were less distinct in male conspecifics

(n = 2) because declines in these individuals were predominately within the 5 % variation

attributable to instrument drift (Fig. 4.5b). Males also increased in Sr:Ba net cps ratios at

younger ages (6 – 8 years) than females (8 – 10 years).


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FIG. 4.5. Typical Sr:Ba ratios with age (years) of bull sharks; total n = 18. Dots indicate potential returns

to less saline waters. a) Female patterns; n = 14. b) Male patterns; n = 2.

Birth bands for adult bull sharks, irrespective of sex, did not group with any of the

hypothesised nursery return periods (marked by spots in Fig. 4.5). Furthermore, they were

more similar to adult birth bands than any nursery elemental signature defined from juvenile

bull sharks (Table 4.4).


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TABLE 4.4. Average squared distance (standard deviation) between adult birth bands and return periods

with nurseries defined by juvenile sharks.

Adult birth Bands Returns

Fitzroy 17.20 (6.11) 22.52 (8.62)

Daly 17.44 (5.88) 23.25 (9.67)

Liverpool 19.86 (5.36) 26.34 (8.68)

E. Alligator 14.68 (5.84) 18.23 (7.42)

Roper 12.79 (7.33) 18.85 (9.93)

Towns 9.32 (5.83) 12.11 (10.58)

Returns 5.89 (3.41) -

In contrast to bull sharks, pigeye nurseries could not be distinguished based on unique multi-

elemental fingerprints (Global R = 0.109, p = 0.084; Table 4.5; Fig. 4.6).

TABLE 4.5. Pairwise ANOSIM correlations among Carcharhinus amboinensis nurseries indicating

whether differences are due to distinct environmental signatures or random variation. WA = Western

Australia, NT = Northern Territory, N QLD = North Queensland.

WA (n = 16) NT (n = 27) N QLD (n = 6)

WA - 0.125 0.146

NT 0.070 - 0.078

N QLD 0.120 0.222 -

R values ranging from ~ 0 (no different) to 1 (highly different) are above the diagonal; probability of

differences arising purely at random are below the diagonal. Sample sizes for each nursery are given.


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SIMPER analyses indentified manganese (55

Mn) as being most characteristic in Western

Australia and least in the Northern Territory. Magnesium (25

Mg) was also a major component

of elemental signatures in Western Australia but lowest in north Queensland. Barium (137

Ba)

showed a complementary pattern, being characteristic in north Queensland and the lowest in

Western Australia signatures (Fig. 4.6).

FIG. 4.6. Similarity Percentages (SIMPER) – element contributions to pigeye shark nursery signatures.

Total n = 49, (WA n = 16, NT n = 27, N QLD n = 6). WA – Western Australia, NT – Northern Territory,

N QLD – North Queensland.

Age-based differences in vertebral microchemistry were not evident in pigeye sharks.

Sr:Ba net cps ratios of juveniles were less variable than those of bull sharks and ranged

between 200 – 450 (Fig. 4.7.). These values remained relatively constant with the onset of

maturity. Any changes in Sr:Ba net cps ratios were subtle, ranging between 100 – 200 and

their frequency did not increase with age. Any declines in Sr:Ba ratios were commonly

within the 5 % variation attributable to instrument drift.


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a)

b)

FIG. 4.7. Typical Sr:Ba net cps ratios (counts per second) with age (years) of pigeye sharks; total n = 39. a)

Female patterns, n = 18; b) Male patterns, n = 21.


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a)

b)

FIG. 4.8. Typical element:Ca net cps ratios (counts per second) with age (years) of pigeye sharks; total n

= 39. I) Lithium II) Magnesium III) Manganese IV) Zinc. a) Female patterns, n = 18; b) Male patterns, n

= 21.


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Similarly oscillations in element:Ca ratios were evident in some elements (e.g. 0.025

to 0.01 for 55

Mn; Fig. 4.8), although predominately within the 5 % variation attributable to

instrument drift. Due to the absence of unique nursery fingerprints, and the lack of a

distinctive nursery phase in element ratios, we did not attempt to identify chemical

fingerprints of the juvenile phase within line scan analysis of adult pigeye sharks.

DISCUSSION

Our results show the potential for chemical signatures within shark vertebrae to track the

long-term (lifetime) movements of these animals within marine, estuarine and freshwater

habitats. Unique elemental signatures between bull shark nurseries supports the assumption

that vertebral microchemistry reflects environmental signatures in some sharks. Declines in

Sr:Ba net cps ratios in mature female bull sharks are consistent with predicted changes in

vertebral microchemistry reflecting periodic returns to freshwater nurseries assuming that

Sr:Ba net cps ratios reflect salinity changes as observed in other estuarine species

(McCulloch et al. 2005, Allen et al. 2009). The lack of this pattern in male conspecifics

suggests this behaviour might reflect pupping events rather than to mate. This is further

supported by the occurrence of this behaviour in 1-2 year cycles correlating with the

estimated 10-11month gestation period and rest year in the reproductive cycle (Rasmussen &

Murru 1992, Last & Stevens 2009).

Assuming vertebral signatures are not altered because of metabolic exchange in the

vertebrae as the individual ages (Campana et al. 2002), none of the adults were born or

resided in any of the nurseries identified from the analysis of the vertebrae of juveniles, and

thus we could not confirm the existence of return to natal areas solely on the basis of

evidence from chemical fingerprints. Unfortunately, comparisons between adult return events


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and their natal fingerprints lacked statistical power due to the limited numbers of return

periods per individual. Furthermore, the relationship between body surface area and

absorption of elements needs to be confirmed before robust conclusions between adult and

juvenile life stages can be drawn.

Gradual declines in some element:Ca net cps ratios (e.g. 0.025 to 0.01 for 55

Mn) in

pigeye sharks are consistent with adult conspecifics moving into deeper water away from

nutrient-rich freshwater, particularly in areas such as northern Australia lacking nutrient

upwellings. The lack of unique nursery signatures in juveniles prevented the identification of

natal return behaviours in this species using vertebral microchemistry. Oceanic waters across

northern Australia might not be chemically distinct due mixing by oceanic and wind driven

currents. In teleost fish, marine habitats can only be discriminated if there are large changes

in water chemistry such as salinity, temperature or water gradients that are often present on

narrower or more steeply sloping shelf systems (Gillanders & Kingsford 2000, Kingsford et

al. 2009, Steer et al. 2009). This might represent a limitation of vertebral microchemistry to

discern movements in some shark species.

Age-specific variation in vertebral microchemistry also correlate with described

changes in habitat use following the same assumptions. Sr:Ba net cps ratios were typically

lowest in bull sharks around the time of birth, in accordance with neonates occupying

freshwater environments. Ratios then increased at 2- 4 years of age as juveniles move into

more saline waters, increasing net cps ratios until maturity. Adults showed the highest ratios,

consistent with the consistent use of marine habitat (Yeiser et al. 2008, Brunnschweiler et al.

2010, Carlson et al. 2010, Heupel et al. 2010b). Similar patterns in element:Ca net cps ratios

in pigeye sharks were not evident, thus reiterating either a limitation of vertebral

microchemistry to map fine-scale movements in sharks or that the use of nurseries is less

defined in this species.


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Surprisingly, vertebral microchemistry was more similar among juveniles and

different among adult life stages between species than predicted. As expected, certain neonate

stages of bull sharks were distinctly lower than equivalent pigeye shark age classes, but other

individuals were almost identical. This suggests a degree of habitat overlap between species

as juveniles and highlights the individual variability among bull sharks. Furthermore, the

greater variation in Sr:Ba net cps ratios in adult bull sharks might reflect the recently defined

broad-scale movement patterns into cooler eastern/western Australian waters and the absence

of such behaviour in pigeye sharks (Brunnschweiler et al. 2010, Carlson et al. 2010).

The incorporation of elements within juvenile bull shark vertebrae highlights the

potential for vertebral microchemistry as a valuable tool for discriminating complex

behaviour such as pupping or natal site fidelity, although clarification of how elements are

conserved in shark vertebrae, lag and cumulation effects, and whether these signatures are

stable through time, are needed (Cailliet & Radtke 1987, Welden et al. 1987). Results suggest

chemical cues could guide female returns to nursery and as such, implies large consequences

should water chemistry change due to altering freshwater flows, or drainage from

surrounding industries.

Conclusively attributing specific behaviour to certain vertebral signatures is beyond

the scope of this study, but because bull sharks inhabit a wide diversity of habitats with

specific age-related patterns of habitat use, changes in vertebral microchemistry should

correlate with these behaviours. Conversely, such habitat-shifting behaviour is not described

in pigeye sharks, leading to the expectation – and our observation – of relatively lower

variation in vertebral microchemistry in this species. Current results highlight the potential

for vertebral microchemistry to describe shark movements between chemically distinct

environments; however, the power of this method will depend on clearer definition of the

mineralisation process in shark vertebrae.


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ACKNOWLEDGEMENTS

Funded by Tropical Rivers and Coastal Knowledge Research Hub, Charles Darwin

University, Darwin and the Australian Institute of Marine Science. Sampling done with the

support of fishermen, Wildlife Resources Inc., Kakadu National Park and the Department of

Resources – Fisheries, Northern Territory.

CHAPTER 5 – Pleistocene isolation, secondary

introgression and restricted contemporary gene flow

in the pigeye shark, Carcharhinus amboinensis

across northern Australia.

Published: Tillett, B.J., Meekan, M.G., Broderick, D., Field, I.C., Cliff, G. and Ovenden, J.R. (2012).

Pleistocene isolation, secondary introgression and restricted contemporary gene flow in the pigeye

shark, Carcharhinus amboinensis across northern Australia. Conservation Genetics. 13: 99-115. DOI:

10.1007/s10592-011-0268-z.

Chapter 5– Population genetic structure / Phylogeography (C. amboinensis)

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INTRODUCTION

Genetic diversity is fundamental to species persistence and hence effective species

management. An understanding of the origin and maintenance of patterns of genetic diversity

requires knowledge of the ecology (mating systems etc) and history of a species across

geological time. The combination of this information offers managers the ability to predict

how genetic diversity may alter in the future, a capacity that is particularly valuable in a

world facing the possibility of large-scale changes in climate and structure of ecosystems.

The coastal habitats of the Indo-Australian archipelago harbour a very diverse range

of elasmobranchs that includes many endemics (White et al. 2006, Last & Stevens 2009).

This diversity is thought to reflect the geological history of the region with large changes in

sea levels during Pleistocene ice ages significantly altering inshore habitats, (Williams et al.

2009) repeatedly fracturing then reconnecting populations. This process of geological change

has been particularly severe for species inhabiting the shallow coastal shelves of northern

Australia. For almost 90 % of the last 150 000 years, the Torres Strait has been closed due to

the formation of a land-bridge between Cape York and Papua New Guinea; isolating eastern

populations from north-west Australia (Voris 2000). The Gulf of Carpentaria was

periodically isolated as one or more freshwater lakes and swamps. Furthermore, sea surface

temperatures were up to 4 C cooler due to changes in the intensity and dynamics of the

Indonesian Throughflow Current; the major current system in the region (Kuhnt et al. 2004,

Williams et al. 2009).

In addition to events occurring in geological time, ecological processes also serve to

structure populations. Complex patterns of habitat use are a good example of such

phenomena, with differential patterns of male migration and female site fidelity or philopatry

restricting gene flow in species such as white (Carchardon carcharias, Pardini et al. 2001),


Page | 73

blacktip (Carcharhinus limbatus, Keeney et al. 2005), bull (C. leucas, Tillett et al. 2012b)

and lemon sharks (Negaprion brevirostris, Feldheim et al. 2002). Other behaviours such as

the distance travelled to find a mate (isolation by distance) can also restrict gene-flow.

Carcharhinus amboinensis is a large (maximal 2.8 m maximum TL within Australia)

high-order predator common in shallow coastal waters of northern Australia, and along

tropical and sub-tropical coasts throughout much of the Indian Ocean (Last & Stevens 2009).

Little demographic data exists for this species and much of our information about its

distribution and abundance may be confounded by problems with accurate species

identification due to morphological similarities between closely related species (Last &

Stevens 2009). The habitat that it occupies predisposes C. amboinensis to the likelihood of

high fishing pressure by growing human populations along coasts abutting most of its range

(Field et al. 2009a, Northern Territory Government 2009). However, we lack sufficient data

to determine the degree of threat to the species, leading to the International Union for the

Conservation of Nature (IUCN) to classify the pigeye shark as ‘Data Deficient’ (IUCN 2010).

The coastal waters of northern Australia remains one of the few strongholds for the species

and offers the opportunity to examine regional patterns in the genetics largely unconfounded

by the effects of selective harvest and declining populations.

This study describes the genetic structure of populations and intra-specific phylogeny

of C. amboinensis across northern Australia. Patterns in mtDNA are compared with

geological history, geographic distance and nuclear DNA markers in order to determine the

relative effects of historical and ecological processes on species genetic diversity. We test

firstly whether the regular formation of land bridges through the Torres Strait during glacial

maxima has resulted in greater mtDNA genetic similarity between populations in Western

Australia and the Northern Territory, than with populations from Queensland. Secondly, if


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this separation facilitated reproductive isolation generating current day sympatric cryptic

species evidenced by genetic differences in nuclear Recombination Activation Gene (RAG 1)

between any identified clades. Thirdly, whether genetic structure is influenced by geographic

distance (Isolation by Distance hypothesis) where individuals from the same area are more

genetically similar than those from distant habitats. And lastly, if patterns of population

genetic structure differ between mtDNA and microsatellite markers commonly associated

with sex-specific behaviours such as female philopatry or male-mediated dispersal. Other

Indian Ocean populations (South Africa and Arabian Sea) were also included as outgroups to

examine regional patterns of genetic diversity.


SAMPLE COLLECTION AND PRESERVATION

Tissue samples of C. amboinensis were obtained within Australia from both commercial

fisheries collected by on-board scientific observers and fishery independent surveys. Samples

were collected near Broome, Western Australia (WA); the north-eastern side of the Joseph

Bonaparte Gulf to the Gulf of Carpentaria (GoC), Northern Territory (NT); Townsville,

North Queensland (Nth QLD) and from Moreton Bay (MB), near Brisbane also in

Queensland. Samples from the Arabian Sea were collected from fish markets in Oman, Qatar

and the United Arab Emirates. Samples from South Africa were collected in the shark control

program of the KwaZulu-Natal sharks board (Cliff & Dudley 1991a; Fig. 5.1).


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FIG. 5.1. Pigeye shark (Carcharhinus amboinensis; total n = 324) capture locations. * Indicates capture

locations. Circles indicate Australian population groupings: 1 = Western Australia (n = 41), 2 = Northern

Territory (n = 205), 3 = Queensland (n = 54). Inset shows other Indian Ocean populations sampled;

4 = South Africa (n = 16); 5 = Arabian Sea (n = 8); PNG = Papua New Guinea (no samples).

Total length distribution of samples is provided below (Fig 5.2). Each sample consisted of

approximately 5 g of white muscle tissue preserved in either 95 % ethanol solution or 10 %

DMSO (dimethylsulphoxide in saturated 5M NaCl solution).


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FIG. 5.2. Frequency distribution of fork length (mm) size classes for pigeye shark (Carcharhinus

amboinensis; n = 300; female n = 141, male n = 159).

In field identifications of species were based on morphological attributes. Due to the

physical similarities among many Carcharhinus spp. and the presence of numerous

congenerics in the region, mtDNA ND4 and control region sequences were compared with

known reference collections including museum voucher specimens where possible.

Sequences from this study were compared against the sandbar (C. plumbeus) (Nardo), the

whitecheek (C. dussumieri) (Müller and Henle), the bignose (C. altimus) (Springer), the

common blacktip (C. limbatus) (Müller and Henle), the Australian blacktip (C. tilstoni)

(Whitley), the graceful (C. amblyrhychoides) (Whitley), the bull (C. leucas) (Müller and

Henle), the spinner (C. brevipinna) (Müller and Henle) and the spot-tail sharks (C. sorrah)

(Müller and Henle).

500-699 900-1099 1300-1499 1700-1899 2199-2299

Length (mm)

Fre

qu

en

cy

01

02

03

04

05

06

07

0

Female

Male


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GENOMIC DNA EXTRACTION

Total Genomic DNA was extracted from 50 mg of preserved tissue using the Chelex method

(Walsh et al. 1991, Estoup et al. 1996). Tissue was placed in a small vial containing a 200 L

solution of 10 % Chelex 100 in TE buffer (5 mM TrisCL pH 8.0 with 0.5 mM EDTA). The

enzyme proteinase K (100 ng) was then added (5 L) to the vial, and heated at 55 oC for

3 hours on a shaking platform to facilitate tissue digestion. The mixture was subsequently

boiled for 8 minutes and then centrifuged at 13000 g for 5 minutes to precipitate the Chelex

resin and bind polyvalent metal ions from the denatured DNA in solution. The supernatant

containing the extracted DNA was then transferred to a fresh vial for manipulation and

storage (Walsh et al. 1991, Estoup et al. 1996).

AMPLIFICATION AND SEQUENCING – MITOCHONDRIAL DNA (mtDNA)

Mitochondrial control region and NADH dehydrogenase subunit 4 (ND4) genes were

selected as these markers are solely maternally inherited (evidencing female movement

patterns) and do not undergo recombination (genetic signatures are not mixed during

reproduction). Genes were amplified from 324 individual C. amboinensis using the

polymerase chain reaction (PCR) methods and sequenced. The 5’ end of the ND4 gene was

amplified and sequenced using the forward primer, ND4

(CACCTATGACTACCAAAAGCTCATGTAGAAGC) (Arevalo et al. 1994) and the reverse

primer, H12293-LEU (TTGCACCAAGAGTTTTTGGTTCCTAAGACC) (Inoue et al. 2001).

Refer to Table 5.1 for PCR reaction conditions. PCR products were purified using

commercial QIAquick PCR purification kits (Qiagen, Doncaster, Vic, Australia) and viewed

on a 1.5 % agarose TAE (containing Tris base, acetic acid and EDTA) gel stained with

ethidium bromide. PCR products were cycle sequenced using ABI Big Dye Terminator v3.1®.


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Fragment separation was carried out by capillary electrophoresis (Applied Biosystems 3130xl)

under conditions recommended by the manufacturer producing 823 base pairs of sequence.

TABLE 5.1. PCR reaction conditions for sequenced DNA. a) PCR reaction mixtures, b) PCR

thermocycling conditions.

a)

ND4 (L) Control region (L) RAG1 (L)

Demineralised water 11.85 11.77 11.85

10x PCR buffer (15 mM MgCl2) 2 2 2

dNTP (2.5 mM) 2 1.28 2

Forward primer (10 M) 1 0.6 1

Reverse primer (10 M) 1 0.6 1

MgCl2 (25 mM) 0 1.6 0

Taq 0.75 units 0.75 units 0.75 units

DNA template 2 2 2

Total reaction volume (l) 20 20 20

b)

Temp (oC) Time

ND4 Control region RAG1 ND4 Control region RAG1

Initial denaturation 94 94 94 1 min 1 min 30 sec 1 min 30 sec

Denaturation 94 94 94 30 sec 10 sec 15 sec

Annealing 50 59 57.5 30 sec 30 sec 30 sec

Extension 72 72 72 30 sec 1 min 1 min

Number of cycles 30 35 30

Final extension 72 72 72 5 min 5 min 5 min

The 5’end of the control region was amplified using the forward primer GWF

(CTGCCCTTGGCTCCCAAAGC) (Pardini et al. 2001) and a reverse primer that was

designed from preliminary C. amboinensis sequence, CL2

(GGAAAAATATACGTCGGCCCTCG). The primer was designed using Primer3 v 0.4.0

(Rozen & Skaletsky 2000). Refer to Table 5.1 for PCR reaction conditions. PCR product


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purification and cycle sequencing followed the same protocol used for the ND4 gene,

although C. amboinensis control regions were sequenced with the designed internal reverse

primer CAR1 (TTTCCAAACCCGGGGTGAGT). Primers were designed following above

methods. A fragment of 831 base pairs was produced.

AMPLIFICATION AND SEQUENCING – NUCLEAR DNA (RECOMBINATION

ACTIVATING GENE 1)

Nuclear Recombination Activating Genes (RAG 1) were amplified to examine whether

present day genetic-mixing occurs between clades identified in mtDNA intra-specific

phylogenies. Thirty Carcharhinus amboinensis (ten from each clade identified in mtDNA)

were amplified using PCR methods and sequenced. The 5’ end of the RAG1 gene was

amplified and sequenced using the designed forward primer, RAG1F

(CCCTCTATAGATGCCTTGCATTG) and the designed reverse primer, RAG1R

(CCAAYTCATARCTTTTGGACTGC). Primers were designed following the fore-

mentioned methods (Rosen & Skaletsky 2000). Refer to Table 5.1 for PCR reaction

conditions. PCR purification and sequencing was performed as described above. A fragment

of 565 base pairs was produced.

AMPLIFICATION AND GENOTYPING – MICROSATELLITES

Unlike RAG1 gene, microsatellites are non-coding sections of DNA and as such conform to

different modes of evolution, providing additional information on genetic exchange. Pig eye

sharks (n = 222) were screened for 14 and genotyped for five microsatellite loci developed

for species (C. tilstoni, C. limbatus, C. plumbeus) other than C. amboinensis (Ovenden et al.


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2006, Portnoy et al. 2007). Loci were selected based on their successful cross-species

amplification (Ovenden et al. 2006) and the highest number of polymorphic alleles between

distant phylogenetic clades. Due to the overall lack of nuclear variation between clades and

population structure identified in mtDNA, the remaining individuals were not assayed.

Amplification was achieved using polymerase chain reaction methods. Reaction mixtures

(total volume of 6 L) contained 1.18 L of milli-Q water; 3 L of 2x QIAGEN Multiplex

PCR Master Mix

(QIAGEN, Doncaster, Vic, Australia) containing a pre-optimised mix of

Taq DNA polymerase, dNTPs and providing a final concentration of 6 mM MgCl2; 0.02 L

of 10 M forward primer with an M13 extension (Schuelke 2000); 0.2 L of 10 M reverse

primer; 0.1 L of fluoro-labelled M13 primer; 1 L of DNA template (12 – 40 ng) and

0.6 L of 5x Q solution

(QIAGEN, Doncaster, Vic, Australia). The DNA template and

reaction mix were initially denatured at 95 oC for 15 mins and then underwent 37 cycles of a

denature period at 94 oC for 30 seconds, an annealing period with loci specific temperatures

of 50 oC for loci CLi-110, CS-10, and CLi-12 and 52 C for loci CS-02 and CS-08 for

45 seconds and an extension time of 72 oC for 1 minute 30 seconds. The thermocycling was

completed with a final extension time of 72 oC for 45 minutes. Loci were individually

amplified but subsequently combined for fragment separation according to label colour and

fragment size. Microsatellite fragment separation and scoring was performed using capillary

electrophoresis (ABI3130xl). The size of microsatellite amplicons (in base pairs) was

calculated to two decimal place and amplicons were allocated to a “bin” that represented the

mean allele size.


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RESTRICTED GENE-FLOW DUE TO PLEISTOCENE SEA-LEVEL CHANGES

Mitochondrial DNA sequences were aligned and edited using the software Geneious™ v4.65

(Drummond et al. 2009). No premature stop codons were identified in the protein coding

ND4 gene. Identical sequences were condensed into unique haplotypes and the

polymorphisms defined by eye and then confirmed using Arlequin v3.11 (Excoffier et al.

2005) and MEGA 4.0 (Kumar et al. 2008) software.

The best fit model of nucleotide substitution and its associated gamma shape for both

mtDNA genes were determined by performing hierarchical likelihood ratio test and by

calculating approximate Akaike Information Criteria using MrModelTest v2.2 (Possada &

Crandall 1998) implemented in Paup 4.0b10 (Swofford 2000).

Populations were defined as the Northern Territory, Western Australia and north

Queensland (Fig. 5.1). As sample sizes from the Gulf of Carpentaria and Moreton Bay were

significantly lower than the above three populations, these were pooled with other Australian

locations. Nine samples from the Gulf of Carpentaria were grouped with the Northern

Territory in accordance with the a priori hypothesis of geographic isolation due to land

bridge formation in the Torres Strait. Eleven individuals from Moreton Bay did not differ

genetically from any of the other locations and therefore grouped with Queensland due to

geographic proximity.

Haplotype diversity (h), (likelihood of randomly choosing two different haplotypes

from the one population), nucleotide diversity (π), (likelihood that two homologous base

positions from two different haplotypes from the same population are different) and the

number of polymorphic sites were estimated for each Australian population (Tajima 1983,

Nei 1987).


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Gene-flow among these populations was examined using F-statistics through a series

of pairwise comparisons using Arlequin v.311 (Excoffier et al. 2005). Patterns of population

genetic structure were quantified by PHIST measures for each gene region separately and

concatenated (supported by the total linkage of the genes due to their common origin within

the mitochondria). This index incorporates the molecular evolution of haplotypes (Tamura

and Nei model of nucleotide evolution gamma corrected) and ranges from 0 (identical allele

frequencies) to 1 (no shared alleles). Analysis of Molecular Variance (AMOVA) was then

used to assess the hierarchical contribution of molecular variance within (PHISC) and among

populations within groups (PHIST); and among groups (PHICT) to the overall measure of

molecular variance.

Connectivity between populations was also examined by measuring evolutionary

distance (Reynold’s D and Slatkin’s D) and the absolute number of migrants (M-values),

again for both genes individually and concatenated. Estimates of evolutionary distance

between populations measures the time in either generation time (Reynold’s D) or

coalescence time (Slatkin’s D) required to generate the observed population pairwise genetic

difference, assuming that the variation between populations’ increases linearly with time

(Reynolds et al. 1983, Slatkin 1995). Similarly, the number of migrants exchanged was

estimated as the exchange of migrants required to generate the observed population pairwise

differences under the assumption that populations were of equal size and the mutation rate

was negligible compared to the migration rate (Slatkin 1991).

Demographic consequences in C. amboinensis populations such as bottlenecks or

expansions due to Pleistocene sea-level changes were investigated using Fu’s Fs and

Tajima’s D statistics. Tajima’s D compared estimates of the mutational parameter (θ) based

on the number of polymorphic sites and the mean number of pairwise differences (Tajima


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1983, 1996). Significant differences in these estimates confirm higher or lower frequency of

haplotypes than expected if mutations were evolving randomly. Fu’s FS calculates the

probability of observing k or less alleles in a neutral (randomly evolving) population, based

on the observed average number of pairwise differences (Fu 1997). A negative value of

either statistic is evidence of an excess of low frequency haplotypes as expected from a recent

population expansion or secondary contact between previously allopatric populations, while a

positive value is evidence for a deficiency of low frequency haplotypes expected from a

recent population bottleneck (Ramos-Onsins & Rozas 2002).

Evidence for either population bottlenecks in microsatellite DNA as shown by

heterozygosity excess or population expansion indicated by heterozygosity deficiency were

analysed using the Wilcoxon test in the program Bottleneck assuming Infinite Alleles Model

(I.A.M) and 10 000 iterations (Cornuet & Luikart 1997).

Phylogenetic support for historic geographic isolation was investigated by

reconstructing intra-specific phylogenies among unique mtDNA haplotypes and mapping

their distribution across northern Australia. Character-based (Neighbour-Joining and

Maximum Parsimony) and model-based (Maximum Likelihood and Bayesian Inference)

methods were used. All analyses were done on each gene region individually and then with

the two gene regions concatenated. Mutations were unweighted and indels were treated as a

fifth state. Indian Ocean and South African populations were included as outgroups.

Maximum Likelihood and Maximum Parsimony analysis were performed using the software

Paup 4.0b10 (Swofford 2000) and Bayesian Inference was performed using the software

MrBayes v3.1(Huelsenbeck & Ronquist 2001). Concatenated sequences were partitioned for

Bayesian Inferences accommodating different models of evolution for each gene region.

Priors for Maximum Likelihood and Bayesian Inference were determined by performing


Page | 84

hierarchical likelihood ratio test and by calculating Akaike Information Criteria using the

software MrModelTest v2.2 (Posada & Crandall 1998). Heuristic tree searches were

performed with 1000 random addition replications and the statistical support for nodes was

determined via 1000 non parametric bootstrap replicates. A majority-rule consensus tree was

also constructed based on the 1000 bootstrap replicates. Bayesian Inference was run using the

Metropolis-coupled Markov Chain Monte Carlo (MCMC) algorithm from randomly

generated starting trees for three million generations, sampling trees every 1000 generations.

Two simultaneous runs were performed with three heated chains and one cold chain each

with a temperature parameter of 0.2. The standard deviation of split frequencies was used as a

convergence diagnostic to determine when the posterior probability distribution had reached

stationarity. The burn-in was set to discard the initial 25 % of samples following guidelines

outlined in the manual. Only Bayesian trees are presented as other phylogenetic

reconstructions produced similar topologies. In addition to conventional phylogenetic

reconstructions, statistical parsimony networks (TCS) were also generated (Clement et al.

2000). Unlike traditional methods, parsimony networks assume that the ancestral haplotype is

present in the current sample, incorporates homoplasy and is not limited to bifurcation at

branch nodes. Gaps were again treated as a fifth state and the connection limit was set to

95 %. Divergence time (million years) between clades was estimated by determining the

percent sequence divergence and then assuming similar mutation rate as defined for lemon,

Negaprion brevirostris (Schultz et al. 2008) and scalloped hammerhead, Sphyrna lewini

sharks converting these values to divergence per million years (Duncan et al. 2006).

Phylogeographic patterns were simplified by graphically representing the frequency

of each clade within Western Australia, the Northern Territory, Gulf of Carpentaria and

North Queensland.


Page | 85

CURRENT DAY SYMPATRIC CRYPTIC SPECIES

RAG 1 sequences were aligned and edited using the software Geneious™ v4.65 (Drummond

et al. 2009). Identical sequences were condensed into unique haplotypes and the

polymorphisms defined by eye and then confirmed using Arlequin v3.11 (Excoffier et al.

2005) and MEGA 4.0 (Kumar et al. 2008) software. Phylogenetic structure identified with

mitochondrial DNA was tested by pairwise comparisons and Analysis of Molecular Variance

(AMOVA).

ISOLATION BY DISTANCE

The ‘Isolation by Distance’ hypothesis was also tested to determine if populations distributed

continuously along the north Australian coastline were structured by geographic distance.

This hypothesis assumes that individuals are not embarking on long-distance travel, and for

this reason genetic distance (PHIST) should increase in a linear fashion with geographic

distance (km). Genetic distances between capture locations (n = 12) were calculated based on

the Tamura and Nei model of nucleotide gamma corrected evolution using Arlequin v.311

(Excoffier et al. 2005) and were correlated using a Mantel test with geographical distances

(by sea). Slope and intercept estimates were subsequently assessed using reduced major

regression analysis using the ‘Isolation by Distance Web Service’ (Jensen et al. 2005). Only

concatenated sequences were compared as they were most variable.


Page | 86

POPULATION GENETIC STRUCTURE – NUCLEAR MARKER (MICROSATELLITES)

Prior to population structure analysis of microsatellite DNA, the null hypothesis of Hardy-

Weinberg equilibrium was tested using Arlequin v3.11 (Excoffier et al. 2005) and

GenAlex v 6.1 (Peakall & Smouse 2005). In addition, the software, Microchecker v. 2.2.3

(van Oosterhout et al. 2004) was implemented to identify possible causes for any deviations

from Hardy-Weinberg equilibrium. Microsatellite genetic diversity was characterised by the

number of alleles per locus (Na), expected (HE) and unbiased (UHE) heterozygosity, observed

heterozygosity (HO) and fixation index (F) using Arlequin v3.11 (Excoffier et al. 2005) and

GenAlex v 6.1 (Peakall & Smouse, 2005). The probability of rejecting the null hypothesis of

genotypic disequilibrium between pairs of loci across populations was estimated by Arlequin

v3.11 (Excoffier et al. 2005). Population structure identified with mitochondrial DNA was

tested by pairwise comparisons and Analysis of Molecular Variance (AMOVA).

RESULTS

RESTRICTED GENE-FLOW DUE TO PLEISTOCENE SEA-LEVEL CHANGES

MtDNA supported genetic similarity between Western Australia and the Northern Territory

and the difference of both of these locations from Queensland. The model of nucleotide

evolution was GTR + I and HKY + I + G (gamma = 0.9871) for ND4 and control region,

respectively. Both mtDNA genes were highly diverse. Fourteen unique ND4 (Table 5.2) and

29 control region haplotypes (Table 5.3) were defined. Subsequent population statistics refer

to concatenated gene regions. Nucleotide diversity (π, as %) was high (0.32 ± 0.19 to

0.89 ± 0.45) compared with other inshore carcharhinids (0.0067 ± 0.0095 to 0.535 ± 0.351;

control region C. sorrah) (Ovenden et al. 2009). Haplotype diversity (h) was slightly higher


Page | 87

in Western Australia and the Northern Territory (0.89 and 0.88 respectively) than the Gulf of

Carpentaria, north Queensland or Moreton Bay (~ 0.6 – 0.76) (Table 5.4).

Three main concatenated mtDNA haplotypes (CN02, CN04 and CN06) were present

in all three main locations but in differing frequencies (Table 5.4). Haplotype CN06 was

overall most abundant decreasing in frequency from Moreton Bay (67 %) to Western

Australia (22 %). Haplotype CN02 displayed a complementary pattern and was most

abundant in Western Australia accounting for 20 % of haplotypes, and decreased in

frequency eastwards so that it accounted for only 4 % of haplotypes in north Queensland.

Haplotype CN04 was similarly abundant in Western Australia, the Northern Territory and

north Queensland (2 – 10 %) but represented 33 % of haplotypes within the Gulf of

Carpentaria. The occurrence of less frequent haplotypes differed between locations but

overall, samples from the Northern Territory and Western Australia had more shared

haplotype frequencies than those from Queensland.

AMOVA confirmed that sharks from Queensland waters were genetically distinct

from those from Western Australia and Northern Territory populations (PHIST = 0.029 p <

0.025; PHIST = 0.025 p < 0.035; PHIST = 0.027 p < 0.027 ND4, control region and

concatenated sequences, respectively; refer to Table 5.5 for population pairwise PHIST

values).

Table 5.2. NADH dehydrogenase 4 (ND4) haplotypes (823 bases); total n = 300; including numbered polymorphic sites, haplotype frequencies, shared haplotypes and indices

of population diversity for Carcharhinus amboinensis. WA = Western Australia; NT = Northern Territory; GoC = Gulf of Carpentaria; QLD = north Queensland. Haplotypes

CAMB_ND415 to CAMBND417 are unique to South African and Arabian Sea populations. GenBank accession numbers HQ393536 HQ393540 & HQ393541 – HQ393553.

1 1 1 1 1 1 2 2 2 2 2 2 3 3 3 3 3 4 4 4 5 5 5 5 5 5 6 6 7 WA NT GoC QLD Moreton

Haplotype 6 0 2 2 8 8 9 3 4 5 5 7 9 0 2 4 6 9 2 8 9 1 6 7 8 8 9 0 4 2

Bay

0 9 6 9 3 7 2 7 7 5 8 9 4 3 7 8 0 9 3 5 7 9 2 0 4 5 1 9 1 4 (n = 41) (n = 196) (n = 9) (n = 48) (n = 6)

CAMB_ND401 C G C C C G C G C C T T T T C C T T A T C T C C A C T T T G 0.366 0.429 0.444 0.542 0.833

CAMB_ND402 T A T * T * T A * * * * * C T * C C * C * * * T * * * * * * 0.439 0.276 0.111 0.042 0.167

CAMB_ND403 * * T * * * * * * * * C * * * T * C * * * * T * G T * * * * 0.146 0.184 0.333 0.146 -

CAMB_ND404 * * T T * * * * * * * C * * * T * C * * * * T * G T * * * * 0.024 0.005 - - -

CAMB_ND405 T A T * T * T A * * * * C C T * C C * C * * * T * * * * * * 0.024 0.071 - 0.104 -

CAMB_ND406 * * T * * * * * * * * C * * * T * C * * * * T * G T * * C * - 0.005 - - -

CAMB_ND407 T A T * T * T A * * * * * C T * C C * C * C * T * * * * * * - 0.015 - - -

CAMB_ND408 * * * * * * * * * * * * * * * * * * * * T * * * * * * * * * - 0.005 - - -

CAMB_ND409 * * * * * * * * * * * * * * * * * * * * * * * * * * * C * * - 0.005 - - -

CAMB_ND410 T A T * T * T A * * * * * C T * C C * C * * * T * * C * * * - 0.005 - - -

CAMB_ND411 * * * * * * * A * * * * * * * * * * * * * * * * * * * * * * - - 0.111 - -

CAMB_ND412 T A T * T * T A * * * * * C T * C C * C * * * T * * * * * A - - - 0.083 -

CAMB_ND413 * * * * * A * * * * * * * * * * * * * * * * * * * * * * * * - - - 0.063 -

CAMB_ND414 * * * * * * * * G * * * * * * * * * * * * * * * * * * * * * - - - 0.021 -

CAMB_ND415 * * * * * * * * * * C * * * * * * * * * * * * * * * * * * * - - - - -

CAMB_ND416 * * * * * * * * * * * * * * * * * * G * * * * * * * * * * * - - - - -

CAMB_ND417 * * * * * * * * * T * * * * * * * * * * * * * * * * * * * * - - - - -

Number of Haplotypes

6 10 4 7 2

Number of Polymorphic sites

20 24 17 21 12

Nucleotide diversity per location (within population, %, ± S.D.)

0.948 ± 0.500

0.923 ± 0.479

0.741 ± 0.416

0.786 ± 0.42

0.496 ± 0.332

Haplotype Diversity per location (within populations, ± S.D)

0.688 ± 0.041

0.705 ± 0.019

0.750 ± 0.112

0.676 ± 0.065

0.333 ± 0.215

Ch

apte

r 5– P

op

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. am

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sis)

TABLE 5.3. Control region haplotypes (831 bases); total n = 300; including numbered polymorphic sites, haplotype frequencies, shared haplotypes and indices of population

diversity for Carcharhinus amboinensis. WA = Western Australia; NT = Northern Territory; GoC = Gulf of Carpentaria; QLD = north Queensland. Haplotypes CAMB_CR30

to CAMB_CR34 are unique to South African and Arabian Sea populations. GenBank accession numbers HQ393554 – HQ393587.

1 2 2 2 2 2 2 2 3 3 3 4 4 6 7 7 7 7 8 8 WA NT GoC QLD Moreton

Haplotype 1 1 1 2 4 4 6 6 7 7 0 7 7 5 8 8 2 2 9 9 0 1

Bay

5 2 5 6 7 2 6 1 8 3 8 4 3 5 1 7 2 2 6 8 9 4 4 (n = 41) (n = 196) (n = 9) (n = 48) (n = 6)

CAMB_CR01 A C T T G A T T T A G T A T T T A A A A C T T 0.024 0.031 - - -

CAMB_CR02 G * C * * T * * * * A C * A * * G * G G T * * 0.220 0.230 0.111 0.229 0.167

CAMB_CR03 G * C * * T * * * * A C * A * * G * G G T * C 0.171 0.066 - - -

CAMB_CR04 * * * * * * * C C * * * * * * * * T * * * * * 0.122 0.092 0.333 0.002 -

CAMB_CR05 * * * * * * * C C * * * * * * * * T * * * * C 0.024 0.056 - - -

CAMB_CR06 * * * * * * * * C * * * * * * * * * * * * * * 0.220 0.265 0.444 0.625 0.667

CAMB_CR07 * * * * * * * C C * * * * * * * * * * * * * * 0.024 0.026 - 0.042 0.167

CAMB_CR08 G * C * * T * * * * * C * A * * G * G * T * * 0.073 0.020 - - -

CAMB_CR09 * * * * * * * * C * * * * * * * * * * * * * C 0.049 0.061 0.111 - -

CAMB_CR10 * * * * * * * * C * * * * * C * * * * * * * C 0.024 0.005 - - -

CAMB_CR11 ? ? ? * * T C * * * A C * A * * G * G G T * * 0.024 - - - -

CAMB_CR12 * * * * * * * * C * * * T * * * * * * * * * * 0.024 0.010 - - -

CAMB_CR13 * * * * * * * * C * * * * * * * * T * * * * * - 0.015 - - -

CAMB_CR14 * * * * * * * * C * * * * * C * * * * * * * * - 0.026 - - -

CAMB_CR15 G * * * * * * * C * * * * * * * * * * * * * * - 0.005 - - -

CAMB_CR16 * * * * * * * C C * * * * * C * * T * * * * * - 0.010 - - -

CAMB_CR17 * * * * * * * * C * * * * * * * * * * * * * C - 0.005 - - -

CAMB_CR18 G * C * * T * * * * A C * A C * G * G * T * C - 0.015 - - -

CAMB_CR19 * * * * * * * * C * * * * * * * * * * * * A * - 0.005 - - -

CAMB_CR20 * * * A * * * C C * * * * * * * * T * * * * * - 0.005 - - -

CAMB_CR21 * * * * * * * * C * * * * * * * * T * * * * C - 0.005 - 0.042 -

CAMB_CR22 G * C * * T * * * * A C * A C * G * G G T * * - 0.010 - - -

Ch

apte

r 5– P

op

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tic structu

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y

(C. a

mb

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ensis)

CAMB_CR23 G * C * * T * * * * * C * A * * G * G G T * C - 0.005 - - -

CAMB_CR24 * * * * * * * * C * * * * * * C * * * * * * * - 0.005 - - -

CAMB_CR25 * * * * * * * C C * * * * * C * * T * * * * C - 0.005 - 0.021 -

CAMB_CR26 G * C * * T * * * T A C * A * * G * G G T * * - 0.005 - - -

CAMB_CR27 G T C * * T * * * * A C * A * * G * G G T * * - 0.005 - -

CAMB_CR28 * * * A * * * * C * * * * * C * * T * * * * * - 0.005 - - -

CAMB_CR29 ? ? ? * * * * C C * A * * * * * * T * * * * * - - - 0.021 -

CAMB_CR30 * * * * A * * * C * * * * * * * * * * * * * * - - - -

CAMB_CR31 * * * * * * * * C * * * * * C * * * G * T * * - - - -

CAMB_CR32 * * * * * * * * C * * * * * * * * * G * T * C - - - -

CAMB_CR33 * * * * * * * * C * * * * * C * * * * *

* C - - - -

CAMB_CR34 * * * * * * * * C * * * T * C * * * G * T * C - - - -


12 27 4 7 3

Number of Polymorphic sites

17 22 14 15 12

Nucleotide diversity per location (within population, %)

0.825 ± 0.440 0.778 ± 0.409 0.451 ± 0.285 0.559 ± 0.310 0.494 ± 0.330

Haplotype Diversity per location (within populations) 0.870 ± 0.026 0.858 ± 0.015 0.750 ± 0.112 0.564 ± 0.069 0.600 ± 0.215

Ch

apte

r 5– P

op

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(C. a

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ensis)


Page | 91

TABLE 5.4. Concatenated (CN) NADH dehydrogenase 4 (ND4; 823 bases) and control region (831 bases); total n = 300; including numbered polymorphic sites, haplotype frequencies, shared haplotypes and indices

of population diversity for Carcharhinus amboinensis. Hap = Haplotype; WA = Western Australia; NT = Northern Territory; GoC = Gulf of Carpentaria; QLD = North Queensland; MB = Moreton Bay. Haplotypes

CN46 – CN52 are unique to South African and Arabian Sea populations.

H 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

WA NT GoC QLD MB

a 1 1 1 1 1 1 2 2 2 2 2 2 3 3 3 3 3 4 4 4 5 5 5 5 5 5 6 6 7 8 8 8 9 0 0 0 0 0 0 1 1 1 1 2 3 5 5 5 6 6 6 6

p 6 0 2 2 8 8 9 3 4 5 5 7 9 0 2 4 6 9 2 8 9 1 6 7 8 8 9 0 4 2 2 3 3 3 5 6 6 8 9 9 0 2 9 9 7 1 0 4 4 2 2 2 3

0 9 6 9 3 7 2 7 7 5 8 9 4 3 7 8 0 9 3 5 7 9 2 0 4 5 1 9 1 4 8 5 8 6 0 5 9 4 1 6 1 7 6 8 4 0 5 5 9 1 2 7 7

CN01 C G C C C G C G C C T T T T C C T T A T C T C C A C T T T G A C T T G A T T T A G T A T T T A A A A C T T 0.02 0.03 - - -

CN02 T A T * T * T A * * * * * C T * C C * C * * * T * * * * * * G * C * * T * * * * A C * A * * G * G G T * * 0.20 0.17 0.11 0.04 0.17

CN03 T A T * T * T A * * * * * C T * C C * C * * * T * * * * * * G * C * * T * * * * A C * A * * G * G G T * C 0.15 0.05 - - -

CN04 * * T * * * * * * * * C * * * T * C * * * * T * G T * * * * * * * * * * * C C * * * * * * * * T * * * * * 0.10 0.09 0.33 0.02 -

CN05 * * T * * * * * * * * C * * * T * C * * * * T * G T * * * * * * * * * * * C C * * * * * * * * T * * * * C 0.02 0.06 - - -

CN06 * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * C * * * * * * * * * * * * * * 0.22 0.27 0.44 0.48 0.67

CN07 * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * C C * * * * * * * * * * * * * * 0.02 0.02 - 0.04 0.17

CN08 * * T T * * * * * * * C * * * T * C * * * * T * G T * * * * * * * * * * * C C * * * * * * * * T * * * * * 0.02 - - - -

CN09 T A T * T * T A * * * * * C T * C C * C * * * T * * * * * * G * C * * T * * * * * C * A * * G * G * T * * 0.07 0.02 - - -

CN10 T A T * T * T A * * * * C C T * C C * C * * * T * * * * * * G * C * * T * * * * A C * A * * G * G G T * * 0.02 0.05 - 0.10 -

CN11 * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * C * * * * * * * * * * * * * C 0.05 0.06 - - -

CN12 * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * C * * * * * C * * * * * * * C 0.02 0.01 - - -

CN13 T A T * T * T A * * * * * C T * C C * C * * * T * * * * * * ? ? ? * * T C * * * A C * A * * G * G G T * * 0.02 - - - -

CN14 * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * C * * * T * * * * * * * * * * 0.02 0.01 - - -

CN15 * * T * * * * * * * * C * * * T * C * * * * T * G T * * C * * * * * * * * * C * * * * * * * * T * * * * * 0.02 0.01 - - -

CN16 * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * C * * * * * C * * * * * * * * - 0.03 - - -

CN17 T A T * T * T A * * * * * C T * C C * C * C * T * * * * * * G * C * * T * * * * A C * A * * G * G G T * * - 0.02 - - -

CN18 * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * G * * * * * * * C * * * * * * * * * * * * * * - 0.01 - - -

CN19 * * T * * * * * * * * C * * * T * C * * * * T * G T * * * * * * * * * * * C C * * * * * C * * T * * * * * - 0.01 - - -

CN20 * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * C * * * * * * * * * * * * * C - 0.01 - - -

CN21 T A T * T * T A * * * * * C T * C C * C * * * T * * * * * * G * C * * T * * * * A C * A C * G * G * T * C - 0.02 - - -

CN22 * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * C * * * * * * * * * * * * A * - 0.01 - - -

CN23 * * T * * * * * * * * C * * * T * C * * * * T * G T * * * * * * * A * * * C C * * * * * * * * T * * * * * - 0.01 - - -

CN24 * * T * * * * * * * * C * * * T * C * * * * T * G T * * * * * * * * * * * * C * * * * * * * * T * * * * * - 0.01 - - -

CN25 * * * * * * * * * * * * * * * * * * * * T * * * * * * * * * * * * * * * * * C * * * * * * * * * * * * * * - 0.01 - - -

CN26 * * * * * * * * * * * * * * * * * * * * * * * * * * * C * * * * * * * * * * C * * * T * * * * * * * * * * - 0.01 - - -

CN27 T A T * T * T A * * * * C C T * C C * C * * * T * * * * * * G * C * * T * * * * A C * A * * G * G G T * C - 0.02 - - -

CN28 * * T T * * * * * * * C * * * T * C * * * * T * G T * * * * * * * * * * * * C * * * * * * * * T * * * * * - 0.01 - - -

CN29 * * T * * * * * * * * C * * * T * C * * * * T * G T * * * * * * * * * * * * C * * * * * * * * T * * * * C - 0.01 - 0.04 -


Page | 92

CN30 T A T * T * T A * * * * * C T * C C * C * * * T * * * * * * G * C * * T * * * * A C * A C * G * G G T * * - 0.01 - - -

CN31 T A T * T * T A * * * * * C T * C C * C * * * T * * * * * * G * C * * T * * * * * C * A * * G * G G T * C - 0.01 - - -

CN32 * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * C * * * * * * C * * * * * * * - 0.01 - - -

CN33 T A T * T * T A * * * * * C T * C C * C * * * T * * C * * * G * C * * T * * * * A C * A * * G * G G T * * - 0.01 - - -

CN34 * * T * * * * * * * * C * * * T * C * * * * T * G T * * * * * * * * * * * C C * * * * * C * * T * * * * C - 0.01 - 0.02 -

CN35 T A T * T * T A * * * * C C T * C C * C * * * T * * * * * * G * C * * T * * * T A C * A * * G * G G T * * - 0.01 - - -

CN36 T A T * T * T A * * * * * C T * C C * C * * * T * * * * * * G T C * * T * * * * A C * A * * G * G G T * * - 0.01 - - -

CN37 * * T * * * * * * * * C * * * T * C * * * * T * G T * * * * * * * A * * * * C * * * * * C * * T * * * * * - 0.01 - - -

CN38 T A T * T * T A * * * * C C T * C C * C * * * T * * * * * * G * C * * T * * * * * C * A * * G * G G T * C - 0.01 - - -

CN39 * * * * * * * A * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * C * * * * * * * * * * * * * C - - 0.11 - -

CN40 * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * C * * * * * * * * * * * * * * - - - 0.02 -

CN41 T A T * T * T A * * * * * C T * C C * C * * * T * * * * * A G * C * * T * * * * A C * A * * G * G G T * * - - - 0.08 -

CN42 * * * * * A * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * C * * * * * * * * * * * * * * - - - 0.06 -

CN43 * * * * * * * * G * * * * * * * * * * * * * * * * * * * * * * * * * * * * * C * * * * * * * * * * * * * * - - - 0.02 -

CN44 * * T * * * * * * * * C * * * T * C * * * * T * G T * * * * * * * * * * * * C * * * * * * * * * * * * * * - - - 0.04 -

CN45 * * T * * * * * * * * C * * * T * C * * * * T * G T * * * * ? ? ? * * * * C C * A * * * * * * T * * * * * - - - 0.02 -

CN46 * * * * * * * * * * C * * * * * * * * * * * * * * * * * * * * * * * * * * * C * * * * * * * * * * * * * * - - - - -

CN47 * * * * * * * * * * * * * * * * * * G * * * * * * * * * * * * * * * A * * * C * * * * * * * * * * * * * * - - - - -

CN48 * * * * * * * * * * * * * * * * * * G * * * * * * * * * * * * * * * A * * * C * * * * * C * * * G * T * * - - - - -

CN49 * * * * * * * * * * * * * * * * * * G * * * * * * * * * * * * * * * A * * * C * * * * * * * * * G * T * C - - - - -

CN50 * * * * * * * * * * * * * * * * * * G * * * * * * * * * * * ? ? ? ? A * * * C * * * * * C * * * * * * * C - - - - -

CN51 * * * * * * * * * T * * * * * * * * * * * * * * * * * * * * * * * * A * * * C * * * * * C * * * * * * * C - - - - -

CN52 * * * * * * * * * * * * * * * * * * G * * * * * * * * * * * * * * * A * * * C * * * T * C * * * G * T * C - - - - -


15 36 4 13 6

Number of Polymorphic sites 36 46 12 37 24


0.89 ±

0.45

0.86 ±

0.43

0.32 ±

0.19

0.68 ±

0.35

0.50 ±

0.31

Haplotype Diversity per location (within populations)

0.89 ±

0.03

0.88 ±

0.02

0.75 ±

0.11

0.76 ±

0.06

0.60 ±

0.22


Page | 93

TABLE 5.5. MtDNA pairwise population PHIST values for Carcharhinus amboinensis (total n = 300).

a) and b) are results from ND4 and control regions individually and c) represents these regions

concatenated . GoC = Gulf of Carpentaria.

a) ND4 Western

Australia

Northern Territory

(incl GoC)

Queensland

(incl Moreton Bay)

(n = 41) (n = 205) (n = 54)

Western Australia - 0.003 0.083

Northern Territory

(incl GoC) 0.285 - 0.029

North Queensland

0.011 0.035 - (incl Moreton Bay)

b) Control region


Northern Territory

(incl GoC) 0.136 - 0.025

North Queensland


c) Concatenated


Northern Territory

(incl GoC) 0.194 - 0.027

North Queensland


PHIST values are above the diagonal and p-values are below the diagonal. Significant values

are indicated in bold.

Connectivity among populations was further supported by the highest evolutionary

distance (Slatkin’s D and Reynold’s D) and fewest exchanges of migrants (M - values)

between Western Australia and Queensland and a complementary pattern between Western

Australia and the Northern Territory (Table 5.6).


Page | 94

TABLE 5.6. Linearised pairwise population PHIST values of Carcharhinus amboinensis (Slatkin’s D,

Reynold’s D and M - value) (total n = 300) . GoC = Gulf of Carpentaria.

Western

Australia

Northern Territory

(incl GoC)

Queensland (incl

Moreton Bay)

(n = 41) (n = 205) (n = 54)

(ND4)

(Control

region)

(ND4)

(Control

region)

Western Australia

Reynold’s D - 0.003 0.015 0.088 0.111

Slatkin’s D - 0.003 0.015 0.092 0.118

M -value - 143.417 33.692 5.414 4.246

Northern

Territory

(incl GoC)

Reynold’s D 0.008 - 0.030 0.026

Slatkin’s D 0.008 - 0.030 0.026

M -value 63.483 - 16.581 19.153

Queensland

(incl Moreton Bay)

Reynold’s D 0.099 0.028 - -

Slatkin’s D 0.104 0.028 - -

M -value 4.820 17.612 - - Results for ND4 and Control region above the diagonal and concatenated below the diagonal

There was no support in any population for bottlenecks or expansion between

previously allopatric populations (indicated by non-significant Tajima’s D and Fu’s FS; Table

5.7). Additionally there was no evidence in microsatellite DNA for influences of Pleistocene

sea-level changes on population structure. One-tailed Wilcoxon tests did not support recent

bottlenecks or expansions (p < 0.84, p < 0.43; p < 0.9, p < 0.15; p < 0.84, p < 0.43

heterozygosity excess and deficiency for Western Australia, Northern Territory and

Queensland populations, respectively).


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TABLE 5.7. Neutrality tests (Tajima’s D and Fu’s FS statistics) for individual Carcharhinus amboinensis

populations (n = 300). Sample sizes; statistics and p values are given. GoC = Gulf of Carpentaria.

Tajima's D statistic (p value)

Fu's FS statistic (p value)

Western Australia (n = 41)

ND4 2.320 (p < 0.99) 10.420 (p < 0.99)

Control region 2.174 (p < 0.99) 1.220 (p < 0.72)

Concatenated 2.410 (p <0.99) 3.258 (p < 0.88)

Northern Territory (incl GoC) (n = 205)

ND4 2.257 (p < 0.99) 10.093 (p < 0.97)

Control region 2.010 (p < 0.98) - 1.699 (p < 0.37)

Concatenated 2.311 (p < 0.98) 0.556 (p < 0.63)

Queensland (incl Moreton Bay) (n = 54)

ND4 0.996 (p < 0.87) 6.424 (p <0.97)

Control region 1.014 (p < 0.87) 4.037 (p < 0.92)

Concatenated 1.078 (p < 0.88) 4.425 (p < 0.91)

Phylogeographic analysis of mtDNA identified three distinct clades of C. amboinensis

across northern Australia. Phylogenies present in ND4 and control region genes are given in

Fig. 5.3 and 5.4 respectively.


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FIG. 5.3. Inferred phylogeny of mitochondrial NADH dehydrogenase subunit 4 (ND4) gene reconstructed

using 95 % statistical parsimony network. Inset Bayesian Inference; rooted with Carcharhinus

amblyrhyncoides and C. leucas, nodal support given as Bayesian posterior probabilities (n = 324).


Page | 97

FIG. 5.4. Inferred phylogeny of mitochondrial control region gene reconstructed using 95 % statistical

parsimony network. Inset Bayesian Inference; rooted with Carcharhinus amblyrhyncoides and C. leucas,

nodal support given as Bayesian posterior probabilities (n = 324).


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Subsequent results refer to concatenated gene regions (Fig. 5.5). Clade one was the most

abundant, while clades two and three were a half and a third as abundant respectively.

FIG. 5.5. Inferred phylogeny of concatenated mitochondrial NADH dehydrogenase subunit 4 (ND4; 823

bases) and control region (831 bases) reconstructed using 95 % statistical parsimony network. Haplotype

numbers from Table 1 are given next to each pie chart. The size of the pie chart represents the frequency of

the haplotype. Inset Bayesian Inference; rooted with Carcharhinus amblyrhyncoides and C. leucas, nodal

support given as Bayesian posterior probabilities (n = 324).


Page | 99

The basal clade (one) was most abundant in the east Australian and western Indian Ocean

populations (South Africa and Arabian Sea) and decreased in frequency moving west along

the Australia coastline. The second clade was equally abundant across Australia, perhaps

more frequent in the Gulf of Carpentaria and not present in both western Indian Ocean

populations, while the third clade was most abundant in Western Australia and decreased in

frequency eastwards. It was also not present in western Indian Ocean populations (Fig. 5.6).

Sequence divergence between clade one and two was 0.24 % and correlated to an isolation

period of 300 000 to 360 000 years during the Pleistocene era. Sequence divergence between

clade one and three was 1.2 % correlating to an isolation period of 1.6 to 2 million years,

again during the Pleistocene (Fig. 5.5).

FIG. 5.6. Frequency of each clade identified in 95 % statistical parsimony network by location for

Carcharhinus amboinensis for each gene region individually, NADH dehydrogenase subunit 4 (ND4; 823

bases) and control region (831 bases); and concatenated. Total n = 324. Dashed circles represent

population structure tested.


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CURRENT DAY SYMPATRIC CRYPTIC SPECIES

All 30 C. amboinensis RAG 1 sequences were the same haplotype.

ISOLATION BY DISTANCE

A Mantel test and reduced major regression analysis for a positive relationship between

genetic similarity and geographic distance based on concatenated gene regions between

capture locations was supported (r = 0.245; p < 0.03; Fig. 5.7).

FIG. 5.7. Reduced major regression analysis between pairwise geographic distances by sea (km) and

pairwise genetic distances (mtDNA PHIST) between un-pooled capture locations (total n = 12). Regression

y = 1.35x -5.487; R2 = 0.059; p < 0.03.


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POPULATION GENETIC STRUCTURE – NUCLEAR MARKER (MICROSATELLITES)

Analysis of microsatellite DNA did not support the population structures identified in

mtDNA (Global FST = -0.00016, p < 0.46). The CS02 microsatellite locus deviated from

Hardy-Weinberg expectations for all Australian populations and was omitted for population

structure and phylogenetic analysis. All other loci remained within these expectations and did

not show evidence of linkage disequilibrium. Mean (± S.E.) sample size of genotypes assayed

with the remaining microsatellite loci for C. amboinensis was 66.42 ± 11.35 over all three

populations and four microsatellite loci. The un-biased heterozygosity was 0.68 ± 0.08. The

mean (± S.E.) number of alleles was 3 ± 0.00 for Cli110, 15.66 ± 3.28 for Cli12, 24.66 ± 0.88

for CS08 and 14.66 ± 3.18 for CS10 (Table 5.8).

TABLE 5.8. The population sample size (N), number of microsatellite alleles per locus (Na), average

observed heterozygosity (Ho), expected heterozygosity (He), unbiased heterozygosity (UHe), fixation

index (F) for Carcharhinus amboinensis; sample sizes for each grouping are given.

Locus N Na Ho He UHe F

Western CLi110 34 3.000 0.235 0.213 0.216 -0.106

Australia Cli12 33 14.000 0.848 0.768 0.780 -0.105

(n = 36) CS08 36 25.000 0.972 0.936 0.949 -0.039

CS10 35 12.000 0.714 0.792 0.804 0.098

Northern CLi110 118 3.000 0.178 0.192 0.193 0.072

Territory Cli12 119 22.000 0.798 0.786 0.789 -0.016

(n = 122) CS08 122 26.000 0.992 0.940 0.944 -0.055

CS10 118 21.000 0.797 0.778 0.781 -0.024

Queensland CLi110 45 3.000 0.378 0.319 0.323 -0.183

(n = 46) Cli12 46 11.000 0.739 0.741 0.749 0.002

CS08 46 23.000 0.978 0.938 0.948 -0.043

CS10 45 11.000 0.689 0.712 0.720 0.032


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DISCUSSION

We provide the first report of the genetic structure of populations of the pigeye shark,

Carcharhinus amboinensis across its known distribution. Despite large total lengths obtained

by C. amboinensis and subsequent potential for mobility, genetic diversity (each

mitochondrial gene region individually and concatenated) was partitioned across northern

Australia so that populations from Western Australia and the Northern Territory grouped

together, separate from Queensland. This Pacific / Indian Ocean barrier is common in coastal

north Australian species (Chenoweth et al. 1998, Lukoschek et al. 2007) and is argued to be a

consequence of the land bridge between Cape York and Papua New Guinea that formed

during the Pleistocene, which formed a barrier to movement and gene flow of marine animals

between east and west coasts of Australia. The unexpected genetic similarity between Gulf of

Carpentaria and Queensland populations suggests that gene-flow across the Torres Strait has

occurred supporting secondary introgression, but due to the low sample size in the Gulf of

Carpentaria we were unable to conclusively quantify the rate of exchange.

The occurrence of a third clade in phylogenetic reconstructions suggests that present

day genetics are not solely shaped by this land bridge which would result in only two groups,

but rather multiple isolating events such as those that occurred during the Pleistocene epoch.

Once these barriers to dispersal were removed gene flow occurred, accounting for the spread

of each clade (albeit in different proportions) among all populations.

The occurrence of clade one in both western Indian Ocean populations suggests that

this is ancestral and that the other two uniquely Australian clades have resulted from recent

isolating mechanisms. The east-west cline in mtDNA haplotype frequencies across northern

Australia in clades one and three suggests earlier isolation of populations with divergence

occurring 1.6 – 2 million years ago during the Pleistocene, followed by more recent


Page | 103

movement, most likely since the last opening of the Torres Strait approximately 6000 years

ago (Voris 2000). Interestingly, despite also evolving independently (divergence time of

300 000 – 360 000 years ago during the Pleistocene era), the second clade does not show an

east-west cline in frequencies, possibly due to different evolutionary constraints. If the higher

abundance of this clade within the Gulf of Carpentaria is not simply due to sampling error,

then this pattern is suggestive of regional isolation that has subsequently dispersed evenly

east and west as sea-levels rose. Conversely, if this elevated abundance does not represent

true frequencies and this clade is evenly distributed across Australia, this may indicate

admixing with Indonesian populations during lower sea-levels that forced previously

allopatric populations together. Further research incorporating a greater number of samples

from within the Gulf of Carpentaria and from Indonesia would tease out the origin of clade

two, and increasing the number of sampling locations across the Indian Ocean would resolve

broad-scale colonisation and dispersal events.

Attributing which of many scenarios in this topographically and hydrologically

complex region has resulted in the current population structure is challenged by the lack of

phylogeographic patterns (indicated by the occurrence of all three clades in each Australian

location) and episodic nature of vicariance events that occurred during the Pleistocene. What

is clear is that the identification of these three clades support multiple incidences of

independent evolution during the Pleistocene and subsequent introgression and movement

across Australia. This movement has mixed and increased population sizes sufficiently to

prevent Tajima’s D and Fu’s FS from detecting changes in allele frequencies due to sudden

reduction or admixing between populations expected during the Pleistocene to generate the

phylogenies, and also prevents further analysis to define likely refugia. Phylogenetic

discontinuities in the absence of current spatial separation as we identified in C. amboinensis


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are rare, but have been recorded by Avise et al. (1987) and attributed to hybrid swarming

arising from secondary contact between allopatrically evolved populations.

The lack of variation among both nuclear markers (RAG 1 and microsatellites)

confirms present day patterns of unrestricted genetic mixing between mitochondrial clades

verifying the absence of a cryptic species of C. amboinensis in north Australia. The lack of

population structure in these markers may indicate sex-specific behaviours such as female

philopatry maintaining mtDNA structure generated through the Pleistocene, although we

cannot eliminate the possibility that not enough time has elapsed (given the generation time

of 13 years and likely effective population size greater than 100 000 individuals) for this

pattern to occur in nuclear markers, reinforcing the idea that genetic structures have an

ancient origin (Frankham et al. 2002). Future studies with increased power in microsatellite

loci are needed to provide robust information on male movement patterns, particularly male-

mediated dispersal. ‘Isolation by Distance’ (only explaining 6 % of variance among

populations) effects indicate contemporary restricted movement of individuals across north

Australia and may explain the susceptibility of C. amboinensis to isolating mechanisms

during geological time. Holistic approaches incorporating physical tags such as satellite and

acoustic tags will also help discriminate dispersal potential and home ranges.

In conclusion, results suggests that coastal changes during the Pleistocene epoch, such

as the repeated formation of land bridges in the Torres Strait, separated east and west

Australian populations of Carcharhinus amboinensis, although this isolation was not

sufficient to generate current day sympatric cryptic species. Phylogenetic re-constructions

indicate that this has occurred multiple times through geological history. In addition to

geological events, ecological processes such as habitat use and distance travelled to find a

mate have also influenced population genetic structure. Discriminating between these causes


Page | 105

is vital for successfully attributing how species genetic diversity is maintained, but is

challenged by not only by the complex ecologies of sharks (different ecological function of

age cohorts and the potential for high mobility), but also complex geology. We caution

hastily simpifying factors driving current population genetic structure providing an example

where geological history is a major contributor. Shark biodiversity in the Indo-Australian

Archipelago requires conservation as regional pressures increase and this hinges on

understanding the origin and maintenance of genetic diversity. Phylogenetic reconstructions

reiterate the susceptibility of C. amboinensis to changes in shallow coastal environments,

such as those that occurred during the Pleistocene, and predicted to occur under a changing

world climate.

ACKNOWLEDGEMENTS


University, Darwin and the Molecular Fisheries Laboratory, Department of Employment,

Economic Development and Innovation, Queensland Government. Sampling was undertaken

with the kind support of fishermen; Wildlife Resources Inc; Kakadu National Park; Fishing

and Fisheries Research, Centre James Cook University; Department of Fisheries - Western

Australia, Fish for the Future, RSK Environment LTD / University of Bangor and the

Department of Resources – Fisheries, Northern Territory. We also thank R. Street and J.

Morgan for their technical expertise and Bioscience North Australia for their support.

CHAPTER 6 – Evidence for reproductive

philopatry in the bull shark, Carcharhinus leucas in

northern Australia

Published: Tillett, B.J., Meekan, M.G., Field, I.C., Thorburn, D and Ovenden, J.R. (2012). Evidence

for reproductive philopatry in the bull shark, Carcharhinus leucas in northern Australia. Journal of

Fish Biology. In press.

Chapter 6– Population genetic structure / Reproductive philopatry (C. leucas)

Page | 106

INTRODUCTION

Philopatry occurs when an animal stays in or returns to a specific location (Mayr 1963, Heist

2004, Duncan et al. 2006). There are many types of this behaviour in sharks, although

reproductive philopatry may be one of the most important (Speed et al. 2010). This occurs

when adults return to specific nurseries to either mate or give birth (Feldheim et al. 2004,

Hueter et al. 2005). Philopatry in sharks has challenged the assumption that populations lack

genetic subdivision due to high mobility and distances travelled by adults (Hueter et al. 2005),

however at present field evidence for this behaviour is very limited (Chapman et al. 2009,

Speed et al. 2010).

Reproductive philopatry has significant implications for species management. If local

extinctions of philopatric species occur, the chance of recovery is greatly reduced as the

likelihood of an individual re-utilising an area is not random (Hueter et al. 2005). For this

reason, each region must be managed as a unique population in order to ensure that habitat

degradation or fishing (artisanal, recreational and commercial) does not significantly affect

local biodiversity. Furthermore, restricted gene-flow increases the genetic diversity of the

meta-population; local extinctions that reduce this diversity can weaken the adaptive potential

of a species (Avise et al. 1987). Where such diversity is created by reproductive philopatry,

the need to manage a species at relatively small spatial scales may not be immediately

evident, since adults can be distributed ubiquitously outside natal areas, giving a false

impression of abundance and genetic connectivity.

This study investigates reproductive philopatry in the bull shark, Carcharhinus leucas

(Müller & Henle) across northern Australia. The species is a large (> 3400 mm total length)

high-order predator, common in shallow tropical and subtropical waters globally (Compagno

1984, Cliff & Dudley 1991b, Last & Stevens 2009). Despite their affinity for shallow water,


Page | 107

adults are capable of large-scale movements and have been tracked on migrations of over

1500 km in the Gulf of Mexico (Brunnschweiler et al. 2010, Carlson et al. 2010). For this

reason, it has been assumed that there is little likelihood of genetic structuring within

populations (Kitamura et al. 1996, Ward 2000, Karl et al. 2011). However, Karl et al. (2011)

recently identified restricted habitat use (philopatry, not reproductive philopatry) in females

in the western Atlantic confirming complex patterns of habitat use in bull sharks.

Bull sharks are designated as ‘Near Threatened’ by the IUCN due to the close

proximity of critical habitats (estuaries and rivers) to anthropogenic influences (IUCN 2010).

Northern Australia is an ideal study location to resolve fine-scale population structure in this

species, as the region has very little urbanisation of coastlines and river systems including

low levels of fishing pressure. This provides an opportunity to study a shark population likely

to be far less disturbed by anthropogenic influences than in most other places within its range.

Catch data and anecdotal evidence suggest gestation requires approximately

10 - 11 months and that litters range in size from 1 – 12 pups (Cliff & Dudley 1991b, Stevens

& McLoughlin 1991, Last & Stevens 2009). The frequency of reproductive cycles is still

unknown although is estimated to be approximately every two years (Rasmussen & Murru

1992). Females return to freshwater / estuarine nursery grounds to pup (Last & Stevens 2009).

Mature bull sharks (> 2 m total length) found in rivers are rarely male (Montoya & Thorson

1982, Snelson et al. 1984, Last & Stevens 2009), suggesting females use this part of their

range for parturition. After pupping, rivers are used as nurseries for bull sharks (Thorburn &

Rowland 2008). Juveniles reside in these areas for ~ 4 years (Thorburn & Rowland 2008) and

the repeated use of these sites across multiple years is consistent with the definition of

nurseries proposed by Heupel et al. (2007). Research to date has not addressed whether the

use of nurseries over multiple breeding cycles generates population genetic structure in bull


Page | 108

sharks (reproductive philopatry).

We tested the extent of reproductive philopatry of bull sharks by comparing the

diversity of two genetic markers (mitochondrial DNA and microsatellites) in juveniles

sampled from nurseries within rivers and their associated estuarine systems across northern

Australia. With an appropriate sampling strategy of nurseries, comparisons of genetic

structure identified in these two types of DNA can be used to investigate sex-specific patterns

of habitat use within the population (Keeney et al. 2005, Portnoy et al. 2010). For example,

genetic population structure identified in mitochondrial DNA (maternally inherited) that is

not present in microsatellites (bi-parentally inherited) can indicate female philopatry

suggesting male-mediated dispersal. We aim to determine 1) whether population structure

exists between closely located nurseries and if this structure is shaped by sex-specific

movement patterns; 2) whether this structure is influenced by either geographic distance or

long-shore barriers to movement such as changes in substratum (phylogeographic patterns);

3) whether populations across northern Australia have been influenced by sea-level changes

in the Pleistocene epoch and 4) estimate long-term effective population size of bull sharks in

northern Australia.


SAMPLE COLLECTION AND PRESERVATION

Tissue samples of bull sharks were obtained from commercial fishers and on-board scientific

observers operating along the Northern Territory coastline in 2009, and during fishery-

independent surveys across northern Australia between 2002 - 2009. Samples were collected

from thirteen northern Australian river systems including the Fitzroy, Robinson, Mitchell and

Ord Rivers from Western Australia; the Daly, East Alligator, Roper, Towns, Limmen, and


Page | 109

Robinson Rivers from the Northern Territory; and from the Mitchell, Mission and Wenlock

Rivers in north Queensland. The Department of Fisheries of the Northern Territory provided

additional samples collected from Blue Mud Bay and to the north of Tiwi Islands in the

Northern Territory (Fig. 6.1). All capture locations, apart from samples collected to the north

of the Tiwi Islands (subsequently referred to as Darwin coastal), were nursery areas. All

individuals caught within rivers were juveniles (< 1200 mm total length) with a mean size

± S.D. of 900 ± 155 mm (approximately 3 years of age) (Thorburn & Rowland 2008, Tillett

et al. 2011a). Each sample consisted of approximately 5 g of white muscle tissue that was

preserved in either 95% ethanol solution or 10% DMSO (dimethylsulphoxide in saturated 5M

NaCl solution). Sample sizes and locations are shown in Fig. 6.1.

Due to the physical similarities among many Carcharhinus spp., and the presence of

numerous con-generic species in the region, mtDNA ND4 and control region sequences were

compared with reference samples and where possible type specimens obtained from the

Northern Territory Museum and laboratory collections. Sequences from the current study

were compared against known sequences of the sandbar (C. plumbeus) (Nardo), whitecheek

(C. dussumieri) (Müller & Henle), bignose (C. altimus) (Springer), common blacktip (C.

limbatus) (Müller & Henle), Australian blacktip (C. tilstoni) (Whitley), graceful (C.

amblyrhychoides) (Whitley), pigeye (C. amboinensis) (Müller & Henle), spinner (C.

brevipinna) (Müller & Henle) and spot-tail sharks (C. sorrah) (Müller & Henle). Where

possible, species identifications were verified by comparison to sequences on Genbank.


Page | 110

FIG. 6.1. Bull shark (Carcharhinus leucas; n = 169) capture locations.

Group 1: Fitzroy, Robinson and Mitchell Rivers, WA (n = 15); Group 2: Ord River, WA and Daly River,

NT (n = 44); Group 3: East Alligator River, NT (n = 22); Group 4: Blue Mud Bay, NT (n = 18); Group 5:

Roper, Towns, Limmen, and Robinson Rivers (n = 27), NT; Group 6: Mitchell, Wenlock and Mission

Rivers, QLD (n = 17); Group 7: Darwin coastal (n = 26).


Page | 111

GENOMIC DNA EXTRACTION

Total Genomic DNA was extracted from 50 mg of preserved tissue using the Chelex method

(Walsh et al. 1991, Estoup et al. 1996). Tissue was placed in a small vial containing a 200 μl

solution of 10 % Chelex 100 in TE buffer (5 mM Tris-Cl pH 8.0 with 0.5 mM EDTA). The

enzyme proteinase K (100 ng) was then added (5μl) to the vial producing a final

concentration of 2.4 ng, and heated at 55oC for 3 hours on a shaking platform to facilitate

tissue digestion. The mixture was then boiled for 8 minutes and centrifuged at 13000 g for

5 minutes to precipitate the Chelex resin and bind polyvalent metal ions from the denatured

DNA in solution. The supernatant containing the extracted DNA was transferred to a fresh

vial for manipulation and storage.

AMPLIFICATION AND SEQUENCING – MITOCHONDRIAL DNA (mtDNA)

The mitochondrial control region and NADH dehydrogenase subunit 4 (ND4) genes were

amplified from 169 individual bull sharks using polymerase chain reaction (PCR) and

sequenced. The 5’ end of the ND4 gene was amplified and sequenced using the forward

primer, ND4 (CACCTATGACTACCAAAAGCTCATGTAGAAGC) (Arevalo et al. 1994)

and the reverse primer, H12293-LEU (TTGCACCAAGAGTTTTTGGTTCCTAAGACC)

(Inoue et al. 2001). Amplification reactions were performed using 20 μl PCR reaction

mixtures containing 11.85 μL of demineralised water; 2 μL of 10x PCR reaction buffer

containing 15 mM MgCl2; 2 μL of 2.5 mM dNTP mix; 1 μL of each 10 μM primer; 0.75

units of Taq DNA polymerase (Sigma Aldrich, Missouri, USA) and 2μL of DNA template.

Thermocycling conditions included an initial denaturation step of 94 ºC for one minute

followed by 30 cycles of a denaturing step at 94 ºC for 30 seconds, an annealing step at 50 ºC

for 30 seconds, and an extension step at 72 ºC for 30 seconds. A final extension step of


Page | 112

5 minutes at 72 ºC completed the thermocycling. PCR products were purified using

commercial QIAquick PCR purification kits (Qiagen, Doncaster, Vic, Australia) and viewed

on a 1.5 % agarose TAE (containing Tris base, acetic acid and EDTA) gel stained with

ethidium bromide. Cycle sequencing reactions used ABI Big Dye Terminator v3.1®.

Fragment separation was carried out by capillary electrophoresis (Applied Biosystems 3130xl)

under conditions recommended by the manufacturer producing 797 base pairs of sequence.

The 5’end of the control region was amplified using the forward primer GWF

(CTGCCCTTGGCTCCCAAAGC) (Pardini et al. 2001) and a reverse primer that was

designed from preliminary C. leucas sequence CL2 (GGAAAAATATACGTCGGCCCTCG).

The primer was designed using Primer3 v 0.4.0 (Rozen & Skaletsky 2000). Amplification

reactions consisted of 20 μl PCR reaction mixtures containing 11.77 μl of demineralised

water; 1.28 μL of 2.5 mM dNTP mix; 2 μL of 10x PCR reaction buffer containing 15 mM

MgCl2; 0.6 μl of each 10 μM primer; 1.6 μl of 25 mM MgCl2; 0.75 units of Taq DNA

polymerase (Sigma Aldrich, Missouri, USA) and 2μl of DNA template. Thermocycling

conditions consisted of an initial denaturation step of 94 ºC for 1 minute 30 seconds followed

by 35 cycles of a denaturation step at 94 ºC for 10 seconds, an annealing step at 59 ºC for

30 seconds, an extension step at 72 ºC for one minute. A final extension step of 5 minutes at

72º C completed the thermocycling. PCR products were purified following the same protocol

used for the ND4 gene. Cycle sequencing reactions and fragment separation also followed the

same procedures as the ND4 gene, although C. leucas control regions were sequenced with

the designed internal reverse primer CLR4 (ATTTCTTTCCAAACTGGGGGAGTC). Again,

this primer was designed using Primer3 v 0.4.0. A fragment of 837 base pairs was produced.


Page | 113

AMPLIFICATION AND GENOTYPING – MICROSATELLITES

Shark samples were screened for 14 and genotyped for three microsatellite loci developed for

shark species other than C. leucas (Ovenden et al. 2006, Portnoy et al. 2007). Loci were

selected based on their successful cross-species amplification (Ovenden et al. 2006) and the

highest number of polymorphic alleles between distant phylogenetic clades. Amplification

was achieved using PCR methods. Reaction mixtures (total volume of 6 L) contained

1.18 L of milli-Q water; 3 L of 2x QIAGEN Multiplex PCR Master Mix

(QIAGEN,

Doncaster, Vic, Australia) containing a pre-optimised mix of Taq DNA polymerase, dNTP’s

and providing a final concentration of 6 mM MgCl2; 0.02 L of 10 M forward primer with

an M13 extension (Schuelke 2000); 0.2 L of 10 M reverse primer; 0.1 L of fluro-labelled

M13 primer; 1 L of DNA template (12 – 40 ng) and 0.6 L of 5x Q solution

(QIAGEN,

Doncaster, Vic, Australia). The DNA template and reaction mix were initially denatured at

95 oC for 15 mins and then underwent 37 cycles of a denature period at 94

oC for 30 seconds,

an annealing period with loci specific temperatures of 50 oC, 52

oC, 58

oC, for loci CPL-90,

CS-08 and CPL-166, respectively for 45 seconds and an extension time of 72 oC for 1 minute

30 seconds. The thermocycling was completed with a final extension time of 72 oC for

45 minutes. Loci were individually amplified but subsequently combined for fragment

separation according to label colour and fragment size. Microsatellite fragment separation

and scoring was performed using capillary electrophoresis (ABI3130xl). The size of

microsatellite amplicons (in base pairs) was calculated to two decimal place and amplicons

were allocated to a group that represented the mean allele size.


Page | 114

POPULATION STRUCTRE AND PHILOPATRY

Population structure and the influence of sex-specific movement patterns was determined by

1) comparing genetic differences identified in mtDNA and microsatellite DNA and

2) comparing the relatedness of juveniles within and between each nursery.

MtDNA control region and ND4 sequences were aligned and edited individually

using the software Geneious™ v4.65 (Drummond et al. 2009). No premature stop codons

were identified in the protein coding ND4 gene. Identical sequences were condensed into

unique haplotypes by eye and then confirmed using Arlequin v3.11 (Excoffier et al. 2005)

and MEGA 4.0 (Kumar et al. 2008) software. Diversity indices for each capture location

were estimated and compared. These included haplotype diversity (h), (likelihood of

randomly choosing two different haplotypes from the one population), nucleotide diversity

(π), (likelihood that two homologous base positions from two different haplotypes from the

same population were different) and the number of polymorphic sites (Tajima 1983, Nei

1987). The best fit model of nucleotide substitution and its associated gamma shape used to

estimate the molecular evolution of gene regions, was determined by performing hierarchical

likelihood ratio test and by calculating approximate Akaike Information Criteria using

MrModelTest v2.2 (Possada & Crandall 1998) implemented in Paup 4.0b10 (Swofford 2000).

Connectivity between capture locations was subsequently assessed using F-statistics

(a measure of population genetic differentiation, Wright 1984)) by a series of pairwise

comparisons and Analysis of Molecular Variance (AMOVA) using Arlequin v.3.11

(Excoffier et al. 2005).

To increase haplotype diversity within capture locations, both genes were

concatenated. This was supported by the total linkage of the genes due to their common

origin within the mitochondria DNA genome. Darwin coastal (n = 26), north of the Tiwi


Page | 115

Islands, was the only capture location that was not a juvenile nursery (solely adults captured),

and as such was omitted from population structure assessment but was included in

phylogeographic reconstructions as a unique haplotype was identified in this location, and

relatedness estimates. Juveniles were assumed not to move between rivers as has been

documented in tracking studies in Caloosahatchee River, southwest Florida, United States of

America (Heupel & Simpfendorfer 2008, Heupel et al. 2010b). Furthermore, evidence

suggests this behaviour is also consistent in northern Australia (Thorburn & Rowland 2008).

Thus, it is assumed that any identified structure is influenced by female movements to

pupping areas rather than juvenile dispersal.

Sample sizes in some capture locations were low. To ensure results were not

confounded by this, F-statistics were initially compared between individual capture locations

then those locations with low sample sizes were pooled. Two types of pooling strategies were

used, (1) by state divisions (Western Australia, Northern Territory and Queensland) to

coincide with fisheries jurisdictions and (2) by geographical proximities (calculated as

kilometres by sea between rivers) and genetic similarities (based on un-pooled pairwise FST

comparison) ensuring genetic relationships between capture locations with larger sample

sizes were not altered. F-statistics confirmed which grouping supported un-pooled genetic

structure. Analysis of Molecular Variance (AMOVA) then tested the hierarchical

contribution of variance and that no significant genetic differences existed within groups.

This grouped Fitzroy, Robinson and Mitchell rivers (group 1) all opening to the Indian Ocean

along the Western Australian coastline, the Ord and Daly rivers (group 2) within the Joseph

Bonaparte Gulf; the East Alligator River (group 3) in the Van Diemens Gulf; Blue Mud Bay

(group 4) was genetically distinct from the East Alligator River and rivers to the east and as

such not pooled; Roper, Towns, Limmen and Robinson Rivers (group 5) on the western side


Page | 116

of the Gulf of Carpentaria; Mitchell, Mission and Wenlock rivers (group 6) on the eastern

side of the Gulf of Carpentaria and the adult population, Darwin coastal (group 7).

The null hypothesis of Hardy-Weinberg equilibrium in microsatellite DNA was tested

using Arlequin v.3.11 (Excoffier et al. 2005) and GenAlex v.6.1 (Peakall & Smouse 2005). In

addition, the software, Microchecker v. 2.2.3 (van Oosterhout et al. 2004) was implemented

to identify possible causes for any deviations from Hardy-Weinberg equilibrium.

Microsatellite genetic diversity was characterised by the number of alleles per locus (Na),

expected (HE) and unbiased (UHE) heterozygosity, observed heterozygosity (HO) and fixation

index (F) using Arlequin v3.11 (Excoffier et al. 2005) and GenAlex v 6.1 (Peakall & Smouse,

2005). The probability of rejecting the null hypothesis of genotypic disequilibrium between

pairs of loci across populations was estimated by Arlequin v3.11 (Excoffier et al. 2005).

Population structure identified with mitochondrial DNA was tested by pairwise comparisons

and Analysis of Molecular Variance (AMOVA) following above protocols.

If females are mating elsewhere and returning to the same location following each

gestation cycle to pup, then juveniles from the same location should be more closely related

to each other than with individuals from surrounding areas. Relatedness among individuals

within and between capture locations was estimated using the software M-L relate

(Kalinoswiki et al. 2006). This software calculates the likelihood that each pair of individuals

are ‘full siblings’, ‘half siblings’, ‘parent-offspring’ or ‘unrelated’ and then reports the

relationship that has the highest likelihood. The average percent of each category within and

between each pooled nurseries was then calculated and compared.


Page | 117

INFLUENEC OF GEOGRAPHIC DISTANCE AND LONG-SHORE BARRIERS TO

MOVEMENT ON POPULATION STRUCTURE

Observed genetic divergence among haplotypes present in Western Australia and north

Queensland raised the question whether genetic structure was driven by isolation by distance.

This was tested by correlating the genetic distance (FST) between all un-pooled capture

locations with the geographical distance (km) by sea. Genetic distances were calculated using

Arlequin v3.11 (Excoffier et al. 2005).

Long-shore barriers impeding the return of females to nursery areas, such as areas of

sub-optimal habitats, could also create genetically distinct sub-groups (Frankham et al. 2002).

Diverse inshore environments exist across northern Australia with the north-west (the

coastline of tropical Western Australia) characterised by large areas of continental shelf and

slope, highly variable tidal regimes and influenced by complex ocean currents (Australian

Government Department of the Environment 2008a). The coastline of the Northern Territory

and Gulf of Carpentaria is characterised by shallow tropical ecosystems with water depths

generally less than 70 m (Australian Government Department of the Environment 2008b).

The influence of substratum as defined by the Australian Government Department of the

Environment was tested by comparing the phylogenetic relationship between individual

rivers / nurseries with similar substratum type (Australian Government Department of the

Environment 2008a, b) and AMOVA pooling nurseries by substratum type.

Phylogeographic patterns were investigated by reconstructing intraspecific

phylogenies among unique mtDNA haplotypes and relating these to geographic locations.

Both character-based (Neighbour-Joining and Maximum Parsimony) and model based

(Maximum Likelihood and Bayesian Inference) methods were used. All analyses were

performed on each gene region individually and then with the two gene regions concatenated.


Page | 118

Mutations were unweighted and in-dels were treated as a fifth state. Outgroups were selected

based on the availability of both ND4 gene and control region sequences, interspecific

genetic similarities and the robustness of topological alternate combinations of outgroups.

Maximum Likelihood and maximum parsimony analysis were performed using the software

Paup 4.0b10 (Swofford, 2000) and Bayesian Inference was performed using the software

MrBayes v3.1(Huelsenbeck & Ronquist 2001). Concatenated sequences were partitioned for

Bayesian Inferences accommodating different models of evolution for each gene region.

Priors for maximum likelihood and Bayesian Inference were determined by performing

hierarchical likelihood ratio test and by calculating Akaike Information Criteria using the

software MrModelTest v2.2 (Posada & Crandall, 1998). Heuristic tree searches were

performed with 1000 random addition replications and the statistical support for nodes was

determined via 1000 non parametric bootstrap replicates. A majority-rule consensus tree was

also constructed based on the 1000 bootstrap replicates. Bayesian Inference was run using the

Metropolis-coupled Markov Chain Monte Carlo (MCMC) algorithm from randomly

generated starting trees for three million generations, sampling trees every 1000 generations.

Two simultaneous runs were performed with three heated chains and one cold chain each

with a temperature parameter of 0.2. The standard deviation of split frequencies was used as a

convergence diagnostics to determine that when posterior probability distribution had reached

stationarity. The burn-in was set to discard the initial 25% of samples following guidelines

outlined in the manual. Only Bayesian trees are presented. In addition to conventional

phylogenetic reconstructions, statistical parsimony networks (TCS) were also generated

(Clement et al. 2000). Unlike traditional methods, parsimony networks assume that the

ancestral haplotype is present in the current sample, incorporates homoplasy and is not

limited to bifurcation at branch nodes. Gaps were again treated as a fifth state and the

connection limit was set to 95 %.


Page | 119

INFLUENCE OF PLEISTOCENE SEA-LEVEL CHANGES ON POPULATION

STRUCTURE

Phylogenetic reconstructions demonstrated a single clade across northern Australia therefore

influences of Pleistocene sea-level changes, such as regional population expansion, was

determined by calculating Tajima’s D and Fu’s FS on all capture locations pooled as one

population using Arlequin v 3.11 (Excoffier et al. 2005). Tajima’s D compares estimates of

the mutational parameter (θ) based on the number of polymorphic sites and the mean number

of pairwise differences (Tajima 1983, 1996). Significant differences in these estimates

confirm deviation of population equilibrium. Whereas Fu’s FS calculates the probability of

observing k or less alleles in a neutral population, based on the observed average number of

pairwise differences (Fu 1997). Significant positive and negative values for both statistics

may be indicative of population bottlenecks or expansions respectively (Ramos-Onsins &

Rozas 2002).

ESTIMATE OF LONG-TERM EFFECTIVE POPULATION SIZE

The mismatch distribution was also estimated under the sudden expansion model and used to

estimate tau (τ) and theta (θ) (Schneider & Excoffier 1999). These values were subsequently

used to roughly estimate time since expansion assuming a divergence rate of 0.67 – 0.8 %

(per million years) calculated from molecular clock estimates for the control region of other

carcharhinids (Duncan et al. 2006, Schultz et al. 2008); and the long-term effective

population size (Gaggiotti & Excoffier 2000) defined as the number of individuals that would

give rise to a loss in genetic diversity at the same rate as the actual population (Frankham et

al. 2002) .


Page | 120

RESULTS

POPULATION STRUCTURE AND PHILOPATRY

Six mitochondrial ND4 (Table 6.1) and thirteen control region (Table 6.2) haplotypes were

described across northern Australia. Models of nucleotide substitution were GTR and HKY+I

for ND4 and the control region, respectively. Neither of these genes required gamma

corrections. Concatenating these two regions increased the number of haplotypes to 18 (Table

6.4). Only results from concatenated sequences are discussed.

Significant mtDNA population genetic structure (adjusted for multiple comparisons

by the false discovery rate method (Narum 2006) existed between individual nurseries (Φ ST =

0.0767; p < 0.0024), but not within (Table 6.3). Genetic differences were not congruent with

fisheries jurisdictions (Φ ST = -0.0602; p < 0.8679). Rather, F-statistics validated the pooling

strategy grouping rivers by geographical proximity and genetic similarity.

Haplotype frequencies differed among pooled nurseries (Table 6.4). Haplotype

CLEU_CN01 was dominant in all locations representing 34 – 94 % of haplotype diversity.

Conversely, most other haplotypes were only present in one or two locations mostly due to

their relative rarity. Haplotype and nucelotide diversity (± S.E) varied between pooled

nurseries measuring lowest in Group 4 (h = 0.1111 ± 0.0964; π = 0.0068 ± 0.0129 and

highest in Group 2 (h = 0.8531 ± 0.0402; π = 0.1514 ± 0.093).

1

TABLE 6.1. NADH dehydrogenase 4 (ND4) haplotypes (797 bases) total n = 169; including numbered polymorphic sites, haplotype frequencies, shared haplotypes

and indices of population diversity; sample sizes for each group are given.

1 4 4 4 5 6 6 6 Group 1 Group 2 Darwin Group 3 Group 4 Group 5 Group 6

Haplotypes 2 2 0 0 2 9 1 2 4 (Fitzroy,

Robinson Daly and Ord

Rivers coastal

East Alligator River

Blue Mud Bay

Roper, Towns, Limmen

Mitchell, Mission

4 0 5 9 0 4 8 1 8

and Mitchell Rivers

and Robinson Rivers

and Wenlock Rivers

(n=15) (n =44) (n=26) (n=22) (n=18) (n=27) (n=17)

CLEU_ND401 C T T C T T C C T 0.067 0.500 0.115 0.318 - 0.111 0.294

CLEU_ND402 * * * T * * * * * 0.867 0.500 0.846 0.682 1.000 0.815 0.706

CLEU_ND403 * C * T * * * * * - - - - - 0.037 -

CLEU_ND404 * * * T * * T * * - - - - - 0.037 -

CLEU_ND405 T * C T C C * T C 0.067 - - - - - -

CLEU_ND406 * * * T * * * T * - - 0.039 - - - -

Number of haplotypes

3 2 3 2 1 4 2

Number of polymorphic sites

7 1 2 1 0 3 1

Nucleotide diversity per location (within population, %) 0.1171 ± 0.0955

0.0642 ± 0.0614

0.0363 ± 0.0442

0.0570 ± 0.0584

0.0000 ± 0.0000

0.0443 ± 0.0497

0.0554 ± 0.0581

Haplotype diversity per location (within populations) 0.2571 ±

0.1416

0.5116 ± 0.0163

0.2800 ± 0.1070

0.4545 ± 0.0777

0.0000 ± 0.0000

0.3390 ± 0.1147

0.4412 ± 0.0977

GenBank accession numbers are HQ324927 to HQ324932 for CLEU_ND401 to CLEU_ND406

Ch

apte

r 6– P

op

ulatio

n ge

ne

tic structu

re / Re

pro

du

ctive ph

ilop

atry (C

. leuca

s)

2

TABLE 6.2. Control region haplotypes (837 bases) total n = 169; including numbered polymorphic sites, haplotype frequencies, shared haplotypes and indices of

population diversity; sample sizes for each group are given.

1 1 2 3 3 4 4 4 6 7 7 7 Group 1 Group 2 Darwin Group 3 Group 4 Group 5 Group 6

Haplotypes 6 9 7 2 7 0 3 4 0 0 4 6

(Fitzroy, Robinson

Daly and Ord Rivers

coastal East Alligator

River Blue Mud

Bay Roper, Towns,

Limmen Mitchell, Mission

7 9 1 2 1 8 1 5 2 3 8 5 and Mitchell

Rivers and Robinson

Rivers and Wenlock

Rivers

(n=15) (n =44) (n=26) (n=22) (n=18) (n=27) (n=17)

CR01 G T G T T C A T G A G A 0.867 0.432 0.692 0.682 0.944 0.556 0.647

CR02 A C * * G * G * A C * * - 0.068 - 0.046 - 0.037 -

CR03 * * * * * * * * * * * G - - - - - 0.185 -

CR04 A C * * G * G C A C * * - - - - - 0.074 -

CR05 A * * * * * G * * * A * 0.067 - - - - 0.074 -

CR06 * * * * * * * * A * * * 0.067 0.205 0.192 0.046 0.056 0.037 0.294

CR07 * * A * * * * * A * * * - 0.159 0.039 0.227 - - -

CR08 * * * * * * * * A T * * - 0.023 - - - - -

CR09 * C A * * * * * A * * * - 0.068 - - - - -

CR10 * * * * * * G * * * * * - - 0.039 - - - -

CR11 * * * * * T * * * * * * - - 0.039 - - - 0.059

CR12 * * * C * * * * * * * * - 0.023 - - - - -

CR13 * * * * * * * * A C * * - 0.023 - - - 0.037 -

Number of haplotypes 3 8 5 4 2 7 3

Number of polymorphic sites

2 9 5 8 1 10 2

Nucleotide diversity per location (within population, %) 0.0455 ± 0.0051

0.2251 ± 0.1455

0.0809 ± 0.0711

0.1634 ± 0.1169

0.0133 ± 0.0252

0.3090 ± 0.1911

0.0668 ± 0.0642

Haplotype diversity per location (within populations) 0.2571 ± 0.1416

0.7526 ± 0.0482

0.4985 ± 0.1039

0.5022 ± 0.1053

0.1111 ± 0.0964

0.6667 ± 0.0887

0.5221 ± 0.1009

GenBank accession numbers are HQ324914 to HQ324926 for CLEU_CR01 to CLEU_CR13.

Po

pu

lation

gen

etic stru

cture

/ Re

pro

du

ctive ph

ilop

atry (C

. leuca

s)


Page | 123

Differences in genetic diversity between pooled nurseries indicated by significant FST

values (adjusted for multiple comparisons by the false discovery rate method) were recorded

between pairwise comparisons (Table 6.5). The greatest pairwise FST value was measured

between groups 4 and 2 (FST = 0.217). The lowest significant FST value was recorded between

groups 5 and 2 (FST = 0.044). The most similar nurseries were groups 4 and 1; though groups

1 and 3 and groups 6 and 3 were also not distinctly different (Table 6.5).

The un-biased heterozygosity (± S.E.) in microsatellite DNA was 0.796 ± 0.0434. The

mean (± S.E.) number of alleles was 4.833 ± 0.307 for locus CPL – 166, 18.333 ± 1.801 for

CPL – 90 and 17.167 ± 1.956 for CS-08 (Table 6.6). None of the capture locations deviated

from Hardy-Weinberg expectations or showed evidence of non-random association of alleles.

4

Ch

apte

r 6– P

op

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n ge

ne

tic structu

re / Re

pro

du

ctive ph

ilop

atry (C

. leuca

s)

TABLE 6.3. Pairwise FST values between un-pooled rivers. FST values are above diagonal, p-values are below diagonal; Significance is indicated in Bold

(significance level p < 0.016 corrected for multiple comparisons by FDR method); total n = 169.

Fitzroy

R, Robinson

R, Mitchell

R, Ord R,

Daly R,

Darwin Coastal,

East Alligator

Blue Mud

Roper R,

Towns R,

Limmen R,

Robinson R,

Mitchell R,

Mission R,

Wenlock R,

WA WA WA WA NT

NT R, NT Bay, NT NT NT NT NT QLD QLD QLD

(n=12) (n=1) (n=2) (n=4) (n=40) (n=26) (n=22) (n=18) (n=10) (n=11) (n=2) (n=4) (n=5) (n=2) (n=10)

Fitzroy R, WA * -1.000 0.383 0.246 0.193 0.036 0.081 -0.030 0.113 0.258 -0.327 0.246 -0.009 0.383 0.163

Robinson R, WA 1.000 * -1.000 -0.667 -0.273 -0.680 -0.578 -1.000 -0.611 -0.300 0.000 -0.667 -1.000 -1.000 -0.500

Mitchell R, WA 0.261 1.000 * -0.191 -0.162 -0.015 -0.200 0.530 -0.069 -0.024 0.000 -0.191 0.015 -0.333 -0.019

Ord R, WA 0.129 1.000 1.000 * -0.029 0.024 0.027 0.371 -0.119 0.016 -0.081 -0.111 -0.007 -0.191 0.003

Daly R, NT 0.000 1.000 1.000 0.552 * 0.075 0.073 0.232 0.035 0.052 0.042 -0.053 0.052 -0.162 0.034

Darwin Coastal, NT 0.169 1.000 0.396 0.196 0.006 * 0.012 0.059 0.014 0.119 -0.177 -0.029 -0.081 -0.054 0.003

East Alligator R, NT 0.128 1.000 1.000 0.313 0.010 0.242 * 0.127 0.030 0.136 -0.122 0.047 -0.029 0.007 0.052

Blue Mud Bay, NT 1.000 1.000 0.194 0.070 0.000 0.084 0.037 * 0.187 0.340 -0.330 0.345 0.061 0.530 0.241

Roper R, NT 0.098 1.000 0.789 1.000 0.124 0.252 0.239 0.025 * -0.034 -0.124 -0.035 -0.028 -0.069 0.028

Towns R, NT 1.000 1.000 0.700 0.439 0.062 0.016 0.030 0.002 0.661 * 0.048 0.016 0.093 -0.024 0.099

Limmen R, NT 1.000 1.000 1.000 1.000 0.501 1.000 1.000 1.000 1.000 0.458 * -0.081 -0.290 0.000 -0.063

Robinson R, NT 0.133 1.000 1.000 1.000 0.798 0.527 0.308 0.064 0.659 0.433 1.000 * -0.007 -0.191 0.003

Mitchell R, QLD 0.508 1.000 0.524 0.534 0.180 1.000 0.580 0.404 0.629 0.239 1.000 0.517 * -0.215 -0.104

Mission R, QLD 0.286 1.000 1.000 1.000 1.000 0.574 0.298 0.219 0.790 0.698 1.000 1.000 1.000 * -0.339

Wenlock R, QLD 0.080 1.000 0.674 0.496 0.140 0.345 0.152 0.017 0.289 0.081 0.639 0.498 1.000 1.000 *

5

Ch

apte

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re / Re

pro

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ctive ph

ilop

atry (C

. leuca

s)

TABLE 6.4. Concatenated mtDNA NADH dehydrogenase 4 (ND4) (797 bases) and control region haplotypes (837 bases) total n =169, total bases = 1634; including

numbered polymorphic sites, haplotype frequencies, shared haplotypes and indices of population diversity for Carcharhinus leucas. Nurseries (excluding group 7)

are pooled by geographic distances and genetic similarities; sample sizes for each grouping are given. Note CLEU_CN18 was only present in low frequencies in the

adult population, Darwin coastal, as such it is only included in phylogenetic reconstructions.

Hap 1 1 1 1 1 1 1 1 1 1 Group 1 Group 2 Group 3 Group 4 Group 5 Group 6 Group 7

4 4 4 5 6 6 6 9 9 0 1 1 2 2 2 3 5 5 5

(Fitzroy, Robinson

Daly and Ord Rivers

East Alligator

River

Blue Mud Bay

Roper, Towns, Limmen

Mitchell, Mission

Darwin coastal

2 0 1 2 9 2 2 5 6 9 6 1 6 0 2 4 9 0 4 6

and Mitchell Rivers

and

Robinson Rivers

and Wenlock Rivers

7 8 2 3 7 1 4 1 4 6 8 9 8 5 8 2 9 0 5 2 (n = 15) (n = 44) (n = 22) (n = 18) (n = 27) (n = 17) (n = 26)

CN01 C T T T T C C T G T G T T C A T G A G A 0.867 0.341 0.682 0.944 0.556 0.647 0.692

CN02 T C * C C * T C * * * * * * * * A * * * 0.067 - - - - - -

CN03 * * C * * * * * * * A * * * * * A * * * 0.067 0.114 0.227 - - - 0.039

CN04 * * C * * * * * * * * * * * * * A C * * - 0.023 - - 0.037 - -

CN05 * * C * * * * * A C * * G * G * A C * * - 0.068 0.046 - 0.037 - -

CN06 * * C * * * * * * * * * * * * * A * * * - 0.114 0.046 - - 0.294 0.077

CN07 * * * * * * * * * * * * * * * * A * * * - 0.091 - 0.056 0.037 - 0.115

CN08 * * C * * * * * * * * * * * * * * * * * - 0.046 - - - - -

CN09 * * * * * * * * * * A * * * * * A * * * - 0.023 - - - - -

CN10 * * C * * * * * * * * * * * * * A T * * - 0.023 - - - - -

CN11 * * C * * * * * * C A * * * * * A * * * - 0.068 - - - - -

CN12 * * * * * * * * * * * C * * * * * * * * - 0.023 - - - - -

CN13 * * * * * * * * * * * * * T * * * * * * - - - - - 0.059 0.039

CN14 * * * * * * * * * * * * * * * * * * * G - 0.046 - - 0.222 - -

CN15 ? * C * * * * * A C * * G * G C A C * * - - - - 0.037 - -

CN16 * * * * * * * * A * * * * * G * * * A * - - - - 0.037 - -

CN17 * * * * * T * * * * * * * * * * * * * * - 0.023 - - 0.037 - -

CN18 * * * * * * T * * * * * * * G * * * * * - - - - - - 0.039

Number of Haplotypes 3 13 4 2 8 3 6

Number of Polymorphic sites 9 12 9 1 12 3 7


0.082 ± 0.060

0.151 ±

0.093

0.111±

0.075

0.007 ±

0.013

0.122 ±

0.079

0.061 ±

0.049

0.055 ±

0.044

Haplotype Diversity per location (within populations)

0.257 ±

0.142

0.853 ±

0.040

0.502 ±

0.105

0.111 ±

0.096

0.658 ±

0.087

0.522 ±

0.101

0.517 ±

0.113


Page | 126

TABLE 6.5. MtDNA pairwise FST values between pooled nurseries for Carcharhinus leucas *

(total n = 143). Nurseries are pooled based on geographic distances and genetic similarities; sample sizes

for each grouping are given.

Group 1 Group 2 Group 3 Group 4 Group 5 Group 6

(Fitzroy, Robinson

and Mitchell Rivers, n = 15)

(Ord and

Daly Rivers, n = 44)

(East Alligator

River, n = 22)

(Blue Mud

Bay, n = 18)

(Roper, Towns,

Limmen and Robinson Rivers, n = 27)

(Mitchell, Mission

and Wenlock Rivers, n = 17)

Group 1 * 0.154 0.028 -0.010 0.096 0.109

Group 2 0.001 * 0.066 0.217 0.044 0.064

Group 3 0.219 0.014 * 0.127 0.062 0.062

Group 4 0.577 0.000 0.038 * 0.159 0.190

Group 5 0.030 0.021 0.043 0.006 * 0.075

Group 6 0.061 0.022 0.101 0.017 0.034 * * FST values are above the diagonal and p-values are below the diagonal. Significant values corrected for

multiple comparisons by False Discovery Rate of p < 0.023 are indicated in bold


Page | 127

TABLE 6.6. The pooled nurseries, sample size (N), number of microsatellite alleles per locus (Na),

average observed heterozygosity (Ho), expected heterozygosity (He), unbiased heterozygosity (UHe),

fixation index (F) for Carcharhinus leucas (total n = 143). Nurseries are pooled based on geographic

distances and genetic similarities; sample sizes for each grouping are given.

Locus N Na Ho He UHe F

Group 1 CPL-166 12 4 0.333 0.462 0.482 0.278

(Fitzroy, Robinson CPL-90 11 11 0.545 0.855 0.896 0.362

and Mitchell CS-08 12 11 0.833 0.813 0.848 -0.026

Rivers, n = 15)

Group2 CPL-166 44 6 0.523 0.463 0.468 -0.129

(Daly and Ord CPL-90 42 22 0.857 0.922 0.933 0.071

Rivers, CS-08 44 22 0.977 0.929 0.940 -0.052

n = 44)

Group 3 CPL-166 24 5 0.625 0.563 0.575 -0.109

(East Alligator CPL-90 24 23 1.000 0.943 0.963 -0.061

River, CS-08 24 19 1.000 0.929 0.949 -0.077

n = 22)

Group 4 CPL-166 19 5 0.526 0.630 0.647 0.165

(Blue Mud CPL-90 18 16 0.944 0.907 0.933 -0.041

Bay CS-08 19 12 0.947 0.888 0.912 -0.067

n = 18)

Group 5 CPL-166 26 4 0.500 0.510 0.520 0.019

(Towns, Roper CPL-90 26 20 0.885 0.889 0.906 0.005

Limmen and CS-08 26 22 0.962 0.939 0.958 -0.024 Robinson Rivers, n

= 27)

Group 6 CPL-166 21 5 0.619 0.503 0.516 -0.230

(Mitchell, Mission CPL-90 19 18 0.842 0.909 0.933 0.073 And Wenlock

River, CS-08 21 17 0.952 0.925 0.948 -0.029

n = 17)

The population structure evident in pooled nursery groups from their mtDNA was not present

in microsatellite DNA (Overall individual FST = -0.0022, p < 0.5485; Overall pooled FST =

0.0056, p < 0.988) or evident in any loci individually (CPL-166: FST = -0.008, p < 0.8; CPL-

90: FST = 0.0125; CS08: FST = 0.0062, p < 0.9). Relatedness between juveniles indicated by

the maximum likelihood that two individuals were “unrelated”, “half-siblings”, “full-siblings”

or “parent-offspring” were no greater within than between pooled locations (Table 6.7.)


Page | 128

TABLE 6.7. Relatedness (%) within and between pooled nurseries and adult population for Carcharhinus

leucas (total n = 169) . Nurseries are pooled based on geographic distances and genetic similarities.

% Unrelated % Half sibling % Full sibling % Parent – Offspring

Within Group 1 75.735 15.441 3.676 5.147

Group 1_Group 2 77.602 15.158 3.394 3.846

Group 1_Group 3 74.706 16.912 4.265 4.118

Group 1_Group 4 76.161 15.17 4.025 4.644

Group 1_Group 5 77.451 18.137 2.451 1.961

Group 1_Group 6 76.471 15.966 2.801 4.762

Group 1_Group 7 77.311 12.605 3.641 6.443

Within Group 2 81.538 12.923 1.846 3.692

Group 2_ Group 3 78.75 16.442 1.538 3.269

Group 2_Group 4 80.162 15.992 1.215 2.632

Group 2_Group 5 80.288 16.827 0.481 2.404

Group 2 _Group 6 77.289 15.568 1.832 5.311

Group 2_Group 7 78.755 12.637 2.015 6.593

Within Group 3 75.897 16.538 2.564 5

Group 3_Group 4 77.895 17.237 1.579 3.289

Group 3_Group 5 75.729 19.167 0.729 4.375

Group 3_Group 6 75.595 17.619 1.667 5.119

Group 3_Group 7 77.738 12.024 2.143 8.095

Within Group 4 69.006 21.637 4.678 4.678

Group 4_Group 5 75.877 21.053 0.658 2.412

Group 4_Group 6 77.057 17.456 0.748 4.738

Group 4_Group 7 76.441 16.541 2.256 4.762

Within Group 5 80.072 16.304 1.449 2.174

Group 5_Group 6 75.746 19.483 0.199 4.573

Group 5_Group 7 78.571 15.675 1.389 4.365

Within Group 6 77.143 18.095 1.429 3.333

Group 6_Group 7 76.644 13.379 1.587 8.39

Within Group 7 76.667 11.429 0.952 10.952






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INFLUENCE OF GEOGRAPHIC AND LONG-SHORE BARRIERS TO MOVEMENT ON

POPULATION STRUCTURE

There was no evidence of isolation by distance between nurseries (Fig. 6.2, p < 0.8623).

FIG. 6.2. Correlation between pairwise geographic distances by sea (km) and pairwise genetic distances

(mtDNA FST) of un-pooled captured locations (total n = 13).

Phylogenetic reconstruction based on the ND4 gene grouped all six haplotypes within one

clade (Fig. 6.3a). Reconstruction based on the control region did not produce well supported

clades (Fig. 6.3b). Concatenating sequences slightly improved resolution although again did

not produce well supported clades (Fig. 6.3c).


Page | 130

a) b)

c)

FIG 6.3. Inferred phylogenies calculated using Bayesian Inference. Nodal support is given as Bayesian

probabilities (n = 169). a) NADH dehydrogenase subunit 4 (ND4); b) control region; c) concatenated ND4

and control region genes.


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The 95 % statistical parsimony network supported one clade structure indicated by one main

haplotype and the occurrence of numerous subsequent haplotypes that are only one or a few

mutational events apart (Fig. 6.4). Two lineages are present represented by haplotypes

CLEU_CN02 separated by five mutational events and haplotypes CLEU_CN05 and

CLEU_CN15 separated by four mutational events. There was no evidence of

phylogeographic structure which would be indicated by all haplotypes from that substratum

type forming tight clusters away from other river systems.

FIG. 6.4. Statistical parsimony network of concatenated NADH dehydrogenase subunit 4 (ND4) and

control region genes (n = 169). The size of each circle represents the relative frequency of each haplotype

and the colour composition depicts the capture locations which haplotypes were identified. Each circle

represents one mutational event; small colourless checks indicate unidentified haplotypes.






Page | 132

Refer to Fig. 6.5 for the 95 % statistical parsimony network of un-pooled capture locations.

Pooling nurseries by substratum type did not produce significant genetic structure

(p < 0.537).

FIG 6.5. Statistical parsimony network of concatenated NADH dehydrogenase subunit 4 (ND4) and

control region genes (n = 169). The size of each circle represents the relative frequency of each haplotype

and the colour composition depicts the capture locations which haplotypes were identified. Each circle

represents one mutational event; small colourless checks indicate unidentified haplotypes.


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INFLUENCE OF PLEISTOCENE SEA-LEVEL CHANGE ON POPULATION

STRUCTURE AND ESTIMATE EFFECTIVE LONG-TERM POPULATION SIZE

Population expansion was evident across northern Australia (Tajima’s D = -1.469 p < 0.04;

Fu’s FS test = -7.490 p < 0.02). The mismatch distribution was unimodal (Fig. 6.6), which

closely matched the expected distributions under the sudden expansion model (Harpending

raggedness index = 0.074 P > 0.05). The tau (τ) value (1.986) roughly estimated 75 – 90

thousand years since expansion. The theta (θ) value (0.293) roughly estimated a female, long-

term effective population size of 11 000 – 13 000.

FIG. 6.6. The observed pairwise difference and the expected mismatch distribution (represented by the

line) under the sudden expansion model of concatenated NADH dehydrogenase subunit 4 (ND4) and

control region haplotypes for all locations pooled as one population for Carcharhinus leucas (n = 169).


Page | 134

DISCUSSION

Our study demonstrates that population structure in mtDNA exists among juveniles sampled

from different freshwater nurseries (mean pair-wise distance by sea between grouped

nurseries ± S.D. = 1083 ± 556 km). In contrast, we found no structure in microsatellite

markers of juveniles at the equivalent spatial scale. These results, combined with the directed

sampling of nurseries and the residence of juveniles within these habitats for approximately 4

years (Simpfendorfer et al. 2005, Heupel & Simpfendorfer 2008, Thorburn & Rowland 2008,

Yeiser et al. 2008, Heupel et al. 2010b, Curtis et al. 2011) supports the hypothesis that

identified population structure is due at the very least to female movement patterns, strongly

suggesting reproductive philopatry. Conclusions are further supported by the absence of

mature males in freshwater / estuaries nurseries (Montoya & Thorson 1982, Snelson et al.

1984, McCord & Lamberth 2009). These results are similar to studies on bull sharks in the

western Atlantic which found restricted patterns of female habitat use, although Karl et al.

(2011) did not directly sample nurseries and therefore could only conclude philopatry, not

reproductive philopatry. Female reproductive philopatry also appears to be the case in other

similar species of carcharhinid sharks such as lemon, Negaprion brevirostris (Rüppell)

(Chapman et al. 2009) and sandbar sharks, Carcharhinus plumbeus (Nardo) (Portnoy et al.

2010). There have been very few studies of this phenomenon and if widespread among

species, it will have important implications for the management of shark populations. Thus,

studies of reproductive philopatry in other coastal sharks must be a priority for future

research.

The lack of population genetic structure in microsatellite DNA that was present in

mtDNA suggests males display different patterns of dispersal than female conspecifics

although the low number of microsatellites assayed in this study may have reduced power


Page | 135

despite comparable sample size (N) and the number of alleles (Na) with other studies

(Feldheim et al. 2001b, Keeney et al. 2005, Ovenden et al. 2009). Equal relatedness within

and between nurseries is expected if microsatellite DNA show no genetic structure, however

loci selected in the current study might not be appropriate for discerning population genetic

structure. An increase in the number of loci assayed (10 -15) by future studies would enhance

information on male movement patterns (dispersal potential). For example, additional

information will help discriminate between males and females both migrating large distances

to find a mate, but females returning to specific freshwater / estuarine nurseries to pup

(similar dispersal potential between sexes) or whether females are truly remaining in closer

proximity to nurseries and males are travelling large-distances to mate indicative of male-

mediated dispersal.

In addition to reproductive philopatry, we investigated the influence of ‘isolation by

distance’ on the genetic structure of C. leucas across the coast of northern Australia. If such

effects occurred and females were commonly straying to nearby rivers when they returned to

pup, then sharks from neighbouring rivers should have been more similar genetically than

those in distant rivers (Wright 1946). However, we found that genetic and geographic

distances were not correlated, suggesting that straying to nearby rivers by females does not

occur frequently enough to increase genetic similarities (Keeney et al. 2005).

Long-shore barriers were also shown not to play any role in the genetic structure of

bull sharks, with a lack of clusters of closely related haplotypes among geologically similar

locations in the 95 % statistical parsimony network and other phylogenetic trees. Results

suggest that the presence of different habitats in coastal environments does not limit

movements of C. leucas sufficiently to structure populations.


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The occurrence of two lineages within the TCS network was consistent with the

possibility of evolution of haplotypes in geographically-isolated populations that have now

become contiguous. These genetic differences among populations probably have a historical

element, reflecting changes in coastal environments that occurred during ice-ages in the

Pleistocene epoch when a land bridge connected Cape York and Papua New Guinea. This

isolated populations on the east coast from those in northern and Western Australia (Voris

2000). These populations became reconnected once sea levels rose at the end of the epoch.

The relatively low long-term effective population size (Ne) of 11 – 13 000 females

calculated from the mismatch distribution (assuming constant mutation rates between

Negaprion brevirostris and Carcharhinus leucas), supports the idea of previous constrictions

of population size due to changes in coastal habitats. Significant Tajima’s D and Fu’ FS

statistics indicate that the population underwent expansion approximately 75 00 000 – 90 000

years ago (also based on the mismatch distribution and assuming equal mutation rates

between N. brevirostris and C. lecuas). Both bottlenecks and expansions occurred during

the Pleistocene, probably reflecting changes in the north Australian coastline during the last

ice age (Voris 2000).

Our work uses a simple genetic approach for the analysis of reproductive philopatry

in sharks. By sampling genetic variation in juveniles resident in nurseries, that may or may

not be connected by dispersal, rather than including multiple locations separated by vicariant

events, we were able to remove the effect of ancient geological history as a casual factor for

observed population genetic structure. Similarly, as conclusions of female reproductive

philopatry are based on genetic structure between juveniles residing in nurseries following

parturition and not solely derived from different population genetic structure in markers, the

likelihood of falsely concluding reproductive philopatry due to marker effects is reduced.


Page | 137

In conclusion, population genetic structure exists between juveniles residing in

closely located nurseries. This heterogeneity in genetic diversity is not attributable to

geological events or isolation by distance, and combined with known life-history parameters

(movements of pregnant females into freshwater / estuarine habitat, utilisation of these areas

by juveniles until ~ 4 years old and limited juvenile movement between nurseries) strongly

supports the prolonged utilisation of specific nurseries by female bull sharks in multiple

breeding events (reproductive philopatry). Furthermore, historical changes in population

dynamics indicate that bull sharks are susceptible to changes in coastal environments

although barriers of sub-optimal habitat are not enough to restrict movement. Results support

growing evidence for the complex behaviours of bull sharks although additional research is

needed to confirm whether these sex-specific differences in behaviour correlate to differences

in dispersal and evolutionary potential.

ACKNOWLEDGEMENTS


University, Darwin and the Molecular Fisheries Laboratory, Department of Employment,

Economic Development and Innovation, Queensland Government. Sampling was undertaken

with the kind support of fishermen, Wildlife Resources Inc, Kakadu National Park and the

Department of Resources – Fisheries, Northern Territory. We also thank D. Broderick, R.

Street and J. Morgan for their technical expertise and Bioscience North Australia for their

support.

CHAPTER 7 – Thesis conclusions

Thesis conclusions

Page | 138

Despite their morphological similarities, differences in genetic population structure and

vertebral microchemistry highlight ecological differences between bull and pigeye sharks

questioning whether pooling of these species in ecological assessments is appropriate. Like

many carcharhinids, both species displayed K-selected life history traits (slow growth, late

attainment of sexual maturity and long-lived) indicative of species that are susceptible to

over-exploitation (Branstetter & Musick 1994, Simpfendorfer et al. 2002, Ardizzone et al.

2006). However, differences in how each species interacted with their physical surroundings

in present (revealed by analysis of vertebral microchemistry) and evolutionary (revealed by

genetic analyses) timescales suggests that these species are not ecologically equivalent. For

bull sharks, intra-specific resource partitioning by sex and size was greater than in pigeye

sharks. Bull sharks had vertebral microchemistry that was highly variable through ontogeny

and genetic population structure was prominent among nurseries, but not across regions

within northern Australia. Pigeye sharks displayed the opposite pattern, with little to no

evidence of intra-specific resource partitioning (by sex or size), consistent vertebral

microchemistry through ontogeny and major differences in genetic population structure that

was clearly evident across regions within northern Australia.

Similar maximum total lengths obtained by both species correlate, as predicted, to

similar age structure and growth dynamics (Cailliet & Goldman 2004). Life history

parameters presented here are, as expected, significantly slower than those recorded for

smaller carcharhinids, such as sharpnose sharks which are characterised by rapid life histories

(Simpfendorfer 1993); although not as delayed as other similarly sized species such as dusky

sharks in which sexual maturity is not attained until ~ 20 years of age (Simpfendorfer et al.

2002).

Surprisingly, similar maximum total lengths of bull and pigeye sharks did not

correlate to similar mobility. Both target species should display similar capabilities to

Thesis conclusions

Page | 139

successfully locate and physically move to and establish in alternative locations (Chin et al.

2010) . However, population genetic structure and phylogeographic assessments indicate that

despite similar abilities, species differ in dispersal potential. The presence of one genetic

clade and evidence of population expansion in bull sharks across northern Australia suggests

current day mixing between coastal populations once isolated during Pleistocene sea-level

changes. Conversely, the occurrence of three clades in pigeye sharks across the same region

suggests unlike bull sharks, present day population connectivity in this species is restricted,

maintaining population structure generated by isolation during the Pleistocene epoch. Such

structure was unexpected as similar studies investigating intra-specific population genetic

subdivisions in coastal sharks (C. obscurus or C. sorrah) across the same region did not

identify any population structure (Ovenden et al. 2009).

Ovenden et al. (2011) provides another example where observed population genetic

structure does not adhere with a priori expectations reiterating the diversity and complexity

of shark behaviours. Smaller sharks are assumed to be physically less capable of travelling

distances recorded for larger species. As such, gene flow between these populations should

be restricted, resulting in genetic subdivisions. The complementary pattern of no genetic

subdivision is predicted for larger species as gene-flow between their populations is readily

exchanged. Ovenden et al. (2011) surprisingly found similar genetic subdivision between two

shark species (one small and one larger) despite contrasting biology; suggesting observed

similarities reflect different frequencies of movements. Smaller species were hypotyhesised

to embark on periodic dispersal events as opposed to common movements between

populations in the larger species.

Population genetic structure and phylogeographic assessments cannot discriminate

infrequent dispersal events, sufficient to maintain genetic similarity from common exchange

of individuals between populations (Avise et al. 1987, Frankham et al. 2002). Tracking

Thesis conclusions

Page | 140

(acoustic or satellite) or vertebral microchemistry which track real-time movements are better

indicators of such behaviour (Campana 1999, Speed et al. 2010). If the general trend for

pigeye sharks is restricted movement as suggested by genetic assessments, vertebral

microchemistry should remain relatively constant. Conversely, if the common behaviour for

bull sharks is indeed unrestricted movements between populations across suboptimal habitats,

the reverse pattern of greater variability in microchemistry is predicted. Current data supports

these hypotheses however further assessments of mineralisation in shark vertebrae such as

how this processes may be affected by an individual physiology or how lag and cumulation

effects are reflected in signatures, is required before robust conclusion can be drawn.

Furthermore, elemental signatures are more prominent from freshwater / estuarine habitats as

opposed to marine environments which could influence that lack of variation in pigeye

vertebral microchemistry (Gillanders 2002, Clarke et al. 2009). Pigeye sharks occupy a very

broad and shallow (40-50 m depth) coastal shelf habitat that due to wind and current-driven

mixing, lacks prominent changes in water chemistry such as nutrient upwelling, temperature

and salinity gradients that are often present on narrower or more steeply-sloping shelf

systems. Such gradients have been shown to contribute to unique chemical fingerprints of

organisms in other marine systems (Gillanders & Kingsford 2000, Kingsford et al. 2009,

Steer et al. 2009). That said, adult bull sharks still displayed greater variability in marine

signatures than adult pigeye sharks. As a coastal species, pigeye sharks may spend their entire

lifetime in shallow waters, rather than embarking on the large migrations that have been

identified in bull sharks (Brunnschweiler et al. 2010, Carlson et al. 2010). Again, future

tagging work is needed to confirm that this result does not merely reflect the limitations of

my analytical technique.

Irrespective of what vertebral microchemistry signatures may represent, starkly

different signatures were found in bull and pigeye sharks supporting different habitat

Thesis conclusions

Page | 141

specificity. Furthermore, the variety of chemical environments (evidenced by Sr:Ba ratios)

occupied by bull sharks suggests although salinity and freshwater inflow have a major

influence on juvenile bull and pigeye shark distribution (Heupel & Simpfendorfer 2008,

Curtis et al. 2011, Knip et al. 2011b), the breadth of chemical tolerances between species

differ, reiterating inter-species ecological diversity.

Even within species there can be considerable variation in ecological roles. Age-based

habitat partitioning in bull sharks, supported by vertebral microchemistry in this thesis is

widely accepted to occur (Yeiser et al. 2008, Heupel & Simpfendorfer 2011) and there is

growing evidence for such behaviours in other similar species (Simpfendorfer & Milward

1993, Feldheim et al. 2002, Heupel et al. 2007, Hussey et al. 2009). Although distinct age- or

sex-specific patterns of habitat use were not evident in the vertebral microchemistry of pigeye

sharks, recent tracking research has suggested increasing affinities for deeper water with

increasing size in juvenile pigeye sharks; however this study had few larger individuals (Knip

et al. 2011a). Other resources, such as diets also differ through ontogeny further reducing

intra-specific competition and contributing to age-specific ecological differences (Bethea et al.

2004, Matich et al. 2010, Sommerville et al. 2011, Werry et al. 2012).

Evidence also suggests intra-specific ecologies differ with individual sex (Anderson

& Pyle 2003), supported by the identification of female philopatry in bull sharks in this thesis.

Such behaviours are increasingly being identified in similar sharks (Hueter et al. 2005,

Keeney & Heist 2006, Jorgensen et al. 2010). Portnoy et al. (2010) has reported female

philopatry and male-mediated dispersal in sandbar sharks; similarly Feldheim et al. (2004)

also concluded sex-specific differences in dispersal in lemon sharks. If anthropogenic-related

mortality in a philopatric species is greater in one sex than the other, resilience would be

skewed by sex-biased reductions in fecundity. Behaviours are not the only recorded

differences in ecologies between individual sexes. Age structure and growth dynamics also

Thesis conclusions

Page | 142

commonly differ (Carlson et al. 1999, Allen & Wintner 2002, Simpfendorfer et al. 2002)

although this was not observed in pigeye sharks (and sample size restrictions prevented sex-

specific comparisons in bull sharks). Regional differences in life history parameters have

even been quantified between populations of the same species (Carlson & Baremore 2005,

Carlson et al. 2006, Romine et al. 2006). Again these differences in life histories translate to

different susceptibilities to overexploitation reiterating the diversity of shark ecologies.

Ecological differences among species imply differences in susceptibilities to

environmental changes predicted under future scenarios such as climate change. Limited

genetic structure, analysis of vertebral microchemistry and other tracking data

(Simpfendorfer et al. 2005, Heupel & Simpfendorfer 2008, Yeiser et al. 2008, Horst 2009,

Brunnschweiler et al. 2010, Carlson et al. 2010, Heupel et al. 2010b) show that bull sharks

are highly mobile and occupy a diverse array of habitats. Consequently, bull sharks can be

classified as having a high adaptability (Chin et al. 2010). Indeed, evidence of this

adaptability is provided by the fact that bull sharks have become a common inhabitant of new

environments such as canal estates created in coastal regions of Australia (Werry et al. 2009).

In contrast, prominent genetic structure and consistent vertebral microchemistry suggest that

pigeye sharks display a more restrictive pattern of habitat use. For this reason, if pigeye

sharks are lost from an area, the chance of re-establishment of a population is less than that of

bull sharks, thus increasing the likelihood of localised depletions (Frankham et al. 2002).

Ultimately, if changes in coastal habitats isolate populations, as occurred during the

Pleistocene Epoch, then speciation is more likely among pigeye than bull sharks because

populations of the former are more genetically distinct than the latter (Avise 1994).

Determining the role that these ecological similarities and differences between bull

and pigeye sharks play in shaping ecosystem dynamics in coastal communities is beyond the

scope of my study. Rather, I confirmed that similarities and differences existed, questioning

Thesis conclusions

Page | 143

the appropriateness of pooling these morphologically similar species in ecological

assessments (Matich et al. 2011). Future modelling of ecological attributes including diet

could determine how functionally similar these species are and whether ecological

differences or similarities are driving ecosystem function and resilience. Predator diversity

and density should also be incorporated into these models as density increases are thought to

promote competition between predators, ultimately resulting in greater diversity (Resetarits &

Chalcraft 2007, Griffin et al. 2008, Heithaus et al. 2008a).

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