distribution, abundance, social and genetic structures of ... · vi chapter 5 – population...

Distribution, abundance, social and genetic structures

of Indo-Pacific bottlenose dolphins (Tursiops aduncus)

in Perth metropolitan waters, Western Australia

Submitted by

Delphine Brigitte Hélène Chabanne

MSc (Montpellier, France)

This thesis is presented for the degree of

Doctor of Philosophy of Murdoch University

School of Veterinary and Life Sciences

2017

i

Declaration

I declare that this thesis is my own account of my research and contains as its main

content work which has not previously been submitted for a degree at any tertiary

education institution.

....................................

Delphine B. H. Chabanne

iii

Abstract

In heterogeneous coastal and estuarine environments, dolphins are exposed to

varying levels of human activities. Consequently, it is important to identify and

characterise fine-scale population structuring based on ecological, social, spatial and

genetic data to develop appropriate conservation and management strategies. This

thesis focused on identifying subpopulations of Indo-Pacific bottlenose dolphins

(Tursiops aduncus) inhabiting Perth waters, Western Australia (WA). Using spatial

and social data collected over four years of boat-based photo-identification surveys,

I: i) estimated abundances, survival and movement rates using a Multistate Closed

Robust Design approach; and ii) examined the social structure and home range using

social association and network analyses. I used microsatellite loci and mtDNA

markers to investigate the genetic population structure of dolphins at metropolitan

(Perth) and regional (c. 1000 km of coastline) scales. High capture probabilities, high

survival and constant abundances described a subpopulation with high fidelity in an

estuary. In contrast, low captures, emigration and fluctuating abundances suggested

transient use and low fidelity in an open coastline region. Overall, dolphins formed

four socially and geographically distinct, mixed-sex subpopulations that varied in

association strength, site fidelity and residency patterns. Curiously, home range

overlap and genetic relatedness did not affect the association patterns. In Perth

metropolitan waters, a source-sink relationship was suggested between a

subpopulation inhabiting a semi-enclosed embayment and three other

subpopulations, including the estuarine subpopulation. On a broader scale, the Perth

metapopulation was genetically distinct from other populations along the WA

southwestern coastline, with little to no migration from and into other populations.

The subpopulations present in Perth waters should each be regarded as a distinct

management unit, with a particular focus on protecting the estuarine subpopulation,

which is small, has limited connection with adjacent subpopulations and is more

vulnerable because of the intensity and diversity of anthropogenic threats present in

the estuary.

v

Statement on the contribution of others

Supervision

Professor Lars Bejder, Doctor Hugh Finn, Professor William Bill Sherwin

Project funding

This research was made possible through the financial commitment of the Swan

River Trust, with additional financial support from the Fremantle Harbour Ports and

Murdoch University.

Stipend

Australian Postgraduate Award (APA)

Murdoch Strategic Top-up

Contribution to data chapters

Chapter 2 – Applying the Multistate Capture-recapture Robust Design to assess

metapopulation structure of a marine mammal: Delphine Chabanne collected and

analysed the data and wrote the manuscript. Professor Kenneth Pollock advised on

the analysis. Professor Kenneth Pollock, Doctor Hugh Finn and Professor Lars

Bejder critically reviewed the manuscript.

Chapter 3 – Identifying the relevant local population for environmental impact

assessments of mobile marine fauna: Delphine Chabanne collected and analysed the

data and wrote the manuscript. Professor Lars Bejder advised on the analysis.

Doctor Hugh Finn and Professor Lars Bejder critically reviewed the manuscript.

Chapter 4 – Genetic structure of socially and spatially discrete subpopulations of

dolphins (Tursiops aduncus) in Perth, Western Australia: Delphine Chabanne

collected and analysed the data and wrote the manuscript. Doctor Simon Allen

contributed to the biopsy data collection. Delphine Chabanne, Doctor Celine Frère

and Bethan Littleford-Colquhoun conducted the genetic laboratory work. Professor

William Sherwin advised on the analysis. Professor William Sherwin, Doctor Hugh

Finn and Professor Lars Bejder critically reviewed the manuscript.

vi

Chapter 5 – Population genetic structure and effective population sizes in Indo-

Pacific bottlenose dolphin (Tursiops aduncus) in southwestern Australia: Delphine

Chabanne analysed the data and wrote the manuscript. Doctor Simon Allen, Anna

Sellas, and Dee McElligot contributed to the biopsy data collection. Claire Daniel

and Doctor Oliver Manlik conducted the genetic laboratory work. Doctor Oliver

Manlik and Professor William Sherwin advised on the analysis. Doctor Oliver

Manlik, Professor William Sherwin, Doctor Hugh Finn and Professor Lars Bejder

critically reviewed the manuscript.

This thesis is presented as a series of four manuscripts in journal format, in addition

to a general introduction and general discussion.

Ethics statement

This study was carried out with approval from the Murdoch University Animal

Ethics Committee (W2342/10 and R2649/14) and was licensed by the

Department of Parks and Wildlife (SF008067, SF008682, SF009286 and

SF009874). Biopsy sampling for molecular analyses were carried out as a part of

broader study, with data collected in accordance with the Murdoch University

Animal Ethics Committee approval (W2076/07; W2307/10; W2342/10 and

R2649/14), and collected under research permits (SF005997; SF006538;

SF007046; SF007596; SF008480; SF009119: SF009734; SF010223) licensed by

Department of Parks and Wildlife.

vii

Acknowledgements

The completion of this PhD marks the end of a long and eventful journey and could

not have been possible without the guidance and support from many people,

especially during the last few months. I have met so many people throughout this

journey: some that were around from the beginning to the end and others that I

briefly met along the way. To everyone, thank you so much for your contribution,

big or small, it has significantly helped me to get through it.

I would like to thank Lars Bejder and Hugh Finn for not giving up on me when I was

about to embark on a different adventure. Your guidance and suggestions during the

planning and writing up of the thesis were invaluable and helped me improve my

critical thinking and scientific writing (well, I hope so!). I’m particularly grateful to

both of you for your understanding and encouragement during my chaotic last few

months. To Lars, thank you for trusting me for many years and giving me the

opportunity to be part of the team (again). To Hugh, thank you for sharing so many

of your anecdotes with me. Our discussions have always helped me to find the right

direction and given me more confidence.

Sincere thanks to Bill Sherwin, for accepting to be my genetic mentor and making

me part of his team. Bill, I have learnt so much over our Skype conversations and the

weekly lab meetings with your students. Also, thanks go to Oliver Manlik for taking

the time to discuss genetic data and provide some advice. To Celine Frère, thank you

for introducing me to the genetic world. You welcomed me in your lab where I had

the privilege to experience the genetic lab work, learn how to process and analyse

data and have brainstorming sessions where your expertise was much appreciated.

A special thanks to Kenneth Pollock for teaching me what I know about mark-

recapture. It has been a privilege to work with you, Ken. Our discussions on

multistate mark-recapture and your enthusiasm for my research have inspired me. I

wish you all the best in your retirement.

viii

I am particularly grateful to Holly Raudino for giving me the opportunity to assist

her during her PhD in Bunbury (WA). You taught me the essentials I needed to

pursue my own project. You helped me to step up in my goals by trusting me and

letting me lead the fieldwork when you were writing up your thesis. You took me

under your wing and always made sure that I was OK and still do. Your ongoing

support means a lot to me.

In that learning process, I’m also very much thankful to Simon Allen for taking the

time to teach me how to biopsy a dolphin and for sharing many of your fieldwork

experiences. I will forever thank you for caring, listening and giving me advice when

unexpected things occurred.

My fieldwork would not have been possible without the dedicated help from many

assistants and interns. Thank you all for keeping the motivation up and countless

hours collecting and processing data. In alphabetical order, thank you to: Ana

Carolina Andrade, Emmeline Audic, Rebecca Bakker, Bronte Bates, Maree Bekkers,

Anna Bendz, Martin Binet, Lisa Binks, Alex Brown, Jacklyn Buchanan, Dennis

Buffart, Joanna Burgar, Anita Byrone, Rene Byrskov, Elizabeth Chabanne, Sharon

Chan, Manon Chautard, Elisa Chillingworth, Lucie Chovrelat, Ana Costa, Alicia

day, Aurelien Delume, Brechtje de Schipper, Georgie Dicks, Gwenael Duclos,

Vivienne Foroughirad, Julia Friese, Hayley Gamble, Beatriz Gemez, Kate Greenfeld,

Olivia Hamilton, Nora Handrek, Daniella Hanf, Emily Hanley, Lisa-Marie Harrison,

Lauren Hawkins, Joe Heard, Nikki Hume, Lonneke IJsseldijk, Kim Jaloustre,

Camille Jan, Vanessa Jaith, Ben Jenhinson, Maria Carmen Jimenez, Martyn Kelly,

Anna Kopps, David Kranitz, Krista Krijger, Alexis Levengood, Wey Yin Lo,

Cassandra Magnin, Jim Maureau, Kelsey Mcclellan, Severine Methion, Micol

Montana, Lisa Mueller, Jesse Murdoch, Nao Nakamura, Krista Nicholson, Harriet

Park, Natasha Prokop, James Raeside, Rita Reis, Nick Riddoch, Nina Schäfer,

Ashleigh Shelton, Lisa Shulander, Claire Smart, Megan Smook, Kate Sprogis,

Nahiid Stephens, Sam Tacey, Alexandra Thibaudeau, Romain Tscheiller, Julian

Tyne, Adam Urwin, Nieki van der Kuijl, Jessica Vermass, Rémi Vignals, Yvette

Vignals, Lizette Viljoen, Timothy Walker, Maike Werner, Imogen Webster, Shona,

Wharton, Dion Whittle and Renae Williams.

ix

For the genetic data, I would like to thank Celine Frère and Bethan Littleford-

Colquhoun for carrying out molecular analyses on the tissue samples I collected in

Perth. I would also like to thank people who have been involved in collecting and

analysing biopsy samples from other dolphin populations along the southwestern

coastline of Western Australia: Simon Allen, Anna Sellas, Dee McElligot, Michael

Krützen and Lars Bejder for biopsy sampling dolphins; Claire Daniel and Oliver

Manlik for carrying out the molecular analyses.

Massive thanks to my fellow lab mates Simon Allen, Alex Brown, Fredrik

Christiansen, Daniella Hanf, Amanda Hodgson, Krista Nicholson, Rob Rankin,

Jenny Smith, Josh Smith, Kate Sprogis, Nahiid Stephens, Julian Tyne, for their time,

collaboration, support, the many useful, or not, discussions during coffee (hot

chocolate for me ) and for sharing their experiences. Special thanks to Julian and

Krista, for making the office less empty, checking on me when not in the office and

for the trips to the science store for more chocolates; and to Nahiid, for making sure

that my belly was full of really yummy food.

I would like to thank the Swan River Trust (now merged with the Department of

Parks and Wildlife) for financial and logistic support throughout many years. Among

many others, thank you to Marnie Giroud, Rachel Hutton, Jason Menzies and Kerry

Trayler for supporting the research work and trusting me. Thank you to Chandra

Salgado-Kent and Sarah Marley for checking on me and the good times sharing our

respective research to the Dolphin Watch volunteers. Thanks also go to the

Fremantle Ports for their annual donations. I would also like to thank the Sailing

Club of Fremantle. Access to the boat ramp and the fuel jetty made our long days out

on the water much easier.

Big thanks to my family back home and friends from all around the world for their

continuous and tremendous support and encouragement as well as their patience and

understanding. A special thank you to Nathalie Long for letting me know Holly was

looking for assistants. This is how my journey down under started. To my fellow

climbers, thank you for adding more adventures in my life and the well-deserved

breaks.

x

This acknowledgement would not be complete if I did not mention Rémi. Thank you

for belaying me when climbing this mountain (aka ‘PhD’), for securing me, and for

taking the rope when I needed breaks. I had always valued your help and advice,

particularly when it came to presentation and design. I also thank you for giving me

many precious memories, taking me exploring and climbing real rocks. Although we

do not partner anymore, I will always care about you.

Last, but definitely not least, a big thank you to my mum and dad. Your ongoing

support and encouragement helped me following my dreams even so it meant going

down under. Je vous aime…

xi

Thesis Publications

The following publications are associated with the chapters of this thesis:

Chabanne, D. B. H., Pollock, K. H., Finn, H. and Bejder L. (2017) Applying the

Multistate Capture-recapture Robust Design to investigate metapopulation structure.

Methods in Ecology and Evolution. DOI: 10.1111/2041-210X.12792. (Chapter 2)

Chabanne, D. B. H., Finn, H. and Bejder L. (2017) Identifying the relevant local

population for environmental impact assessments of mobile marine fauna.

Frontiers in Marine Science, 4: 148. DOI: 10.3389/fmars.2017.00148. (Chapter

3)

http://journal.frontiersin.org/article/10.3389/fmars.2017.00148/full

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Table of contents

Declaration ................................................................................................................... i

Abstract ..................................................................................................................... iii

Statement on the contribution of others .................................................................. v

Acknowledgements ................................................................................................... vii

Thesis Publications .................................................................................................... xi

List of Tables .......................................................................................................... xvii

List of Figures ........................................................................................................... xx

List of Appendices ................................................................................................ xxiii

Glossary ................................................................................................................... xxv

List of abbreviations and symbols ....................................................................... xxvi

Chapter 1. General Introduction ....................................................................... 1

1.1. Characterising the population structure of small cetaceans in coastal and

estuarine environments .............................................................................................. 1

1.2. The Indo-Pacific bottlenose dolphin .............................................................. 8

1.3. Indo-Pacific bottlenose dolphin population in Perth metropolitan waters 9

1.4. Objectives and structure of the thesis ......................................................... 12

1.5. Ethics statement ............................................................................................ 13

Chapter 2. Applying the multistate capture-recapture robust design to

assess metapopulation structure ............................................................................. 15

2.1. Abstract .......................................................................................................... 15

2.2. Introduction ................................................................................................... 17

2.3. Materials and methods ................................................................................. 22

2.3.1. Field methods ........................................................................................... 22

2.3.2. Statistical methods ................................................................................... 26

2.4. Results ............................................................................................................ 27

2.4.1. Effort ........................................................................................................ 27

2.4.2. Model selection ........................................................................................ 27

2.5. Discussion ....................................................................................................... 33

2.5.1. Spatial heterogeneity................................................................................ 33

2.5.2. Apparent survival and abundance estimates ............................................ 34

xiv

2.5.3. Transitions ............................................................................................... 35

2.5.4. Limitations ............................................................................................... 35

2.5.5. Conclusion ............................................................................................... 37

Appendices ................................................................................................................ 38

Chapter 3. Identifying the relevant local population for environmental

impact assessments of mobile marine fauna .......................................................... 57

3.1. Abstract .......................................................................................................... 57

3.2. Introduction ................................................................................................... 58

3.3. Materials and methods ................................................................................. 62

3.3.1. Study area ................................................................................................ 62

3.3.2. Data collection ......................................................................................... 65

3.3.3. Association patterns ................................................................................. 66

3.3.4. Community structure and dynamic .......................................................... 67

3.3.5. Spatial distribution of communities ......................................................... 68

3.3.6. Residence time ......................................................................................... 69

3.3.7. Genetic relatedness .................................................................................. 69

3.4. Results ............................................................................................................ 70

3.4.1. Effort and group size ............................................................................... 70

3.4.2. Community structure and dynamic .......................................................... 71

3.4.3. Association patterns ................................................................................. 72

3.4.4. Spatial distribution of communities ......................................................... 79

3.4.5. Residence time ......................................................................................... 82

3.4.6. Genetic relatedness .................................................................................. 82

3.5. Discussion....................................................................................................... 85

3.5.1. Community segregation ........................................................................... 87

3.5.2. Using social, ecological, and genetic data to evaluate the impact of

developments and activities on the relevant local population: four case studies ... 89

3.5.3. Conclusions ............................................................................................. 93

Appendices ................................................................................................................ 94

Chapter 4. Genetic structure of socially and spatially discrete

subpopulations of dolphins (Tursiops aduncus) in Perth, Western Australia .. 105

4.1. Abstract ........................................................................................................ 105

4.2. Introduction ................................................................................................. 106

xv

4.3. Materials and methods ............................................................................... 108

4.3.1. Genetic sample collection ...................................................................... 108

4.3.2. DNA extraction and sexing.................................................................... 110

4.3.3. Genotyping and validation of microsatellites ........................................ 110

4.3.4. Mitochondrial (mt) DNA sequencing .................................................... 111

4.3.5. Assessment of genetic differentiation .................................................... 111

4.3.6. Assessment of genetic population structure........................................... 111

4.3.7. Analysis of gene flow ............................................................................ 112

4.3.8. Assessment of genetic diversity ............................................................. 112

4.4. Results .......................................................................................................... 113

4.4.1. Validation of genotypes and haplotypes ................................................ 113

4.4.2. Genetic differentiation ........................................................................... 115

4.4.3. Genetic population structure .................................................................. 116

4.4.4. Gene flow and dispersal ......................................................................... 118

4.4.5. Sex-biased dispersal ............................................................................... 118

4.4.6. Genetic diversity .................................................................................... 119

4.5. Discussion ..................................................................................................... 122

4.5.1. Genetic structure and gene flow ............................................................ 123

4.5.2. Conservation implications ..................................................................... 126

4.5.3. Conclusion ............................................................................................. 128

Appendices .............................................................................................................. 129

Chapter 5. Population genetic structure and effective population sizes in

Indo-Pacific bottlenose dolphin (Tursiops aduncus) along the southwestern

coastline of Western Australia .............................................................................. 135

5.1. Abstract ........................................................................................................ 135

5.2. Introduction ................................................................................................. 136

5.2.1. Genetic structure .................................................................................... 136

5.2.2. Effective population size and effective/census population size ratio .... 138

5.3. Materials and methods ............................................................................... 140

5.3.1. Study site and sample collection ............................................................ 140

5.3.2. DNA extraction and Microsatellite genotyping ..................................... 142


5.3.4. Genetic differentiation and population structure ................................... 143

5.3.5. Gene flow ............................................................................................... 144

5.3.6. Effective population (Ne) sizes .............................................................. 144


5.4.2. Genetic differentiation and population structure ................................... 146

xvi

5.4.3. Contemporary gene flow ....................................................................... 150

5.4.4. Effective population sizes ...................................................................... 152

5.5. Discussion..................................................................................................... 154


5.5.2. Genetic differentiation and structure ..................................................... 155

5.5.3. Contemporary gene flow ....................................................................... 156

5.5.4. Contemporary effective population size and Ne/Nc ratio ....................... 157

5.5.5. Conclusions and future research ............................................................ 160

Appendices .............................................................................................................. 162

Chapter 6. General Conclusions .................................................................... 175

6.1. Keys research findings for each subpopulation (Figure 6.1) .................. 176

6.1.1. The Swan Canning Riverpark estuarine subpopulation ........................ 177

6.1.2. The semi-enclosed embayment subpopulations: Owen Anchorage and

Cockburn Sound ................................................................................................... 178

6.1.3. Bottlenose dolphins occurring along the open Gage Roads coastline ... 179

6.2. Theoretical and practical limitations of the study ................................... 182

6.2.1. Fine-scale delineation of subpopulations .............................................. 182

6.2.2. Ecological characteristics of Gage Roads: are there sufficient data? .... 182

6.2.3. Genetic sampling is not random ............................................................ 183

6.2.4. Contemporary effective population size (Ne) and ratio of effective/census

population size (Ne/Nc): unresolved conservation tools ....................................... 184

6.3. Management recommendations ................................................................. 185

6.4. Concluding remarks ................................................................................... 188

References ............................................................................................................... 191

xvii

List of Tables

Table 2.1. Estimates of transition probability ψ (SE) between sites for (a) Scenario 1

(three sites: SCR = Swan Canning Riverpark, CS/OA = Cockburn Sound/Owen

Anchorage, GR = Gage Roads) and (b) Scenario 2 (two sites: SCR vs. Coastal). .... 12

Table 3.1. Number of groups and group size (mean (SE), minimum and maximum)

by geographic region. ................................................................................................. 70

Table 3.2. The mean association indices for the population overall and per

community (ComA, ComB, ComC and ComD); the measure of social differentiation

(S); and the correlation coefficient of the true and estimated association matrices (r).

.................................................................................................................................... 73

Table 3.3. Association indices within and among sex classes (Mantel test, 0.05 one-

side). ........................................................................................................................... 74

Table 3.4. Average strength, clustering coefficients, and affinity (SE) with

comparisons from random calculating using half-weight indices for individuals

sighted at least five times. .......................................................................................... 75

Table 3.5. Sex class (female, male, unknown sex) permutation tests for preferred

(HWI CV) and avoided (proportion of non-zero indices) associations for the

population overall and per community. ..................................................................... 76

Table 3.6. Bootstrap mean (and standard error) genetic relatedness (Queller and

Goodnight, 1989) for within-community (bold) and with individuals from other

communities of bottlenose dolphins (between-community) genotyped with ten

microsatellite loci in Perth metropolitan waters, WA. ............................................... 84

Table 3.7. Correlation coefficient R and ANOVA test between relatedness

coefficient and HWI pairwise within-community...................................................... 84

Table 3.8. Summary comparison of social, temporal, spatial, residency and genetic

patterns across the four communities (ComA, ComB, ComC and ComD). Social

differentiation is described using measures of strength (i.e., measure of

gregariousness); the clustering coefficient (i.e., degree of connection between

associates); and affinity (i.e., the strength of the associates). .................................... 86

Table 4.1. Microsatellite diversity in Indo-Pacific bottlenose dolphins inhabiting

Perth metropolitan waters. ....................................................................................... 114

Table 4.2. Pairwise fixation indices between four genetic clusters previously defined

in STRUCTURE analysis based on ten microsatellite loci and mtDNA control

sequences. Microsatellite FST values are above the diagonal; Mitochondrial ΦST

values are below. Mitochondrial FST values were similar to ΦST, and thus are not

shown here; values that are significant after sequential Bonferroni correction are in

bold. Values in italic were significant before correction (P-value < 0.05). ............. 115

xviii

Table 4.3. Pairwise comparisons of Shannon mutual information index (SHUA)

among socio-geographic subpopulations based on ten microsatellite loci (allele

frequency differences, above diagonal) and mtDNA control region sequences

(haplotype frequency differences, below diagonal). Significant P-values after

sequential Bonferroni correction and based on statistical testing of 999 random

permutations are shown in bold. Values in italic were significant before correction

(P-value < 0.05). ...................................................................................................... 116

Table 4.4. Mean (standard deviation) of the posterior distribution of the

contemporary migration rates (m) in BayesAss (Wilson and Rannala 2003) among

four bottlenose dolphin socio-geographic subpopulations in Perth metropolitan

waters. The subpopulations of which each dolphin belongs are listed in the rows,

while the subpopulations from which they migrated are listed in the columns. Values

along the diagonal (in bold) are the proportions of non-immigrants from the origin

subpopulation for each generation. Moderate estimated migration rates (m > 0.10)

are displayed in italic. .............................................................................................. 118

Table 4.5. Sex-specific FST values based on microsatellite loci and mtDNA for all

and for each pairwise socio-geographic subpopulations. Significance levels of

genetic differentiation between socio-geographic subpopulations are also indicated

for FST values (estimated with Arlequin, after Bonferroni correction: * P-value <

0.05). ........................................................................................................................ 119

Table 4.6. Genetic diversity measures (SE) for bottlenose dolphin socio-geographic

subpopulations using microsatellite loci (n = 10) and mtDNA. .............................. 120

Table 4.7. Summary statistics of various tests to detect a recent bottleneck effect

based on mtDNA control region (Tajima’s D and Fu’s Fs) and microsatellite loci

(SMM: stepwise mutation model. TPM: two-phased model). The Wilcoxon test

found no significant heterozygosity excess after Bonferroni correction (Pcrit = 0.012).

.................................................................................................................................. 122

Table 5.1. Genetic diversity for microsatellite loci in bottlenose dolphin sampled in

eight localities. ......................................................................................................... 146

Table 5.2. Genetic differentiation (FST) among eight bottlenose dolphin sampling

localities in southern Western Australia. ................................................................. 147

Table 5.3. Mean (and 95% CI) recent migration rates inferred using BayesAss 3.0

(Wilson and Rannala 2003). The migration rate is the proportion of individuals in a

population that immigrated from a source population per generation. Values of CI

that do not overlap with zero are in bold. Values along the diagonal (underlining) are

the proportions of non-immigrants from the origin subpopulation for each

generation. ................................................................................................................ 151

Table 5.4. Estimates of the contemporary effective population size before correction

(Ne, 95% CI) and after correction (cNe, 95% CI), ratio cNe/Nc (SE) for localities with

known census population size (Nc, , 95% CI), and estimated eNc (95% CI) for

populations with unknown Nc. The critical value varied according to the sample size

xix

(n > 25, Pcrit = 0.02; n > 50, Pcrit = 0.01). cNe was corrected for overlapping

generation by adding 15 or 10% of the estimates based on the Pcrit, respectively. .. 153

xx

List of Figures

Figure 1.1. Practical approaches (with considerations) and scientific information

used to identify putative ‘units to conserve’ (subpopulations or local populations) in

a population of a small cetacean species...................................................................... 7

Figure 1.2. The three areas previously studied for bottlenose dolphins in the

metropolitan waters of Perth (Western Australia) and the scale of the current study

(red square, 2011-15). Area 1 – Open coastline (more effort in dark purple area than

light purple area, Waples 1997); Area 2 – Cockburn Sound (green, Finn 2005); Area

3 – Swan Canning Riverpark (blue, Chabanne et al. 2012). RI = Rottnest Island;

SCA = Scarborough. .................................................................................................. 11

Figure 2.1. Traditional closed robust design (CRD) vs. multistate closed robust

design (MSCRD) approaches to characterise metapopulation structure and dynamics

through demographic parameters. Both approaches allow estimation of abundance

(N), apparent survival rate (φ) and emigration and immigration [solid arrows] either

time varying (t, t+1, t+2, etc.) or constant. In addition, MSCRD models estimate any

transition probabilities ψ [dashed arrows] between subpopulations associated with

states (i.e., geographic sites). ..................................................................................... 20

Figure 2.2. Map of the metropolitan waters of Perth, Western Australia, showing the

systematic survey routes within each site: the estuary SCR = Swan Canning

Riverpark and the coastal sites (south to north) CS/OA = Cockburn Sound/Owen

Anchorage, and GR = Gage Roads. Within the coastal sites, surveys were conducted

by rotating between three pre-defined transect routes (full, long dash and short dash

lines) to maximise the coverage. ................................................................................ 24

Figure 2.3. Capture probability (p) yielded by the models for each secondary

occasion represented as box plot (min; Quartile 1; median; Quartile 3; max) for each

Scenario: 1 – three sites (SCR = Swan Canning Riverpark, CS/OA = Cockburn

Sound/Owen Anchorage, GR = Gage Roads); and 2 – two sites (SCR and Coastal).

.................................................................................................................................... 29

Figure 2.4. Seasonal estimated abundances (Ntotal ± 95% confidence intervals) for

(a) Scenario 1 – three sites (SCR = Swan Canning Riverpark, CS/OA = Cockburn

Sound/Owen Anchorage, GR = Gage Roads) and (b) Scenario 2 – two sites (SCR

and Coastal). Lines between data points have been used for illustrative purposes

only; continuity of values is not implied. Sites are as follows: SCR (red), CS/OA

(yellow), GR (purple) and Coastal in Scenario 2 (grey). ........................................... 30

Figure 3.1. Maps of the study area showing: (a) the transect routes per geographic

regions (GR = Gage Roads, SCR = Swan Canning Riverpark, OA = Owen

Anchorage, CS = Cockburn Sound) with locations of past, current, and proposed

developments (1- pile driving; 2- dredging; 3- desalination; 4- outer harbour); and

(b) the bathymetry (in meters) with the locations of the groups sighted during the

xxi

systematic surveys conducted from 2011 to 2015, including mixed groups (i.e., mix

of individuals from different communities). .............................................................. 54

Figure 3.2. Network diagram for 129 bottlenose dolphins using the HWI. The shape

of each node indicates its sex (circle: females; square: males; triangle; unsexed), and

the colour of each node indicates its unit defined by the modularity of Newman

(2006) (purple ComA; red ComB; green ComC; yellow and orange sub-communities

D and D’), although three individuals were assigned to two different units depending

on the method (Newman vs. Hierarchical linkage). Only links representing

affiliations (HWI > 0.16) are shown, and link width is proportional to index weight.

Node size is based on the betweenness centrality measure of each individual. ......... 72

Figure 3.3. Lagged association rates (LAR) for all individuals (black line) and

within the communities (ComA purple; ComB red; ComC green; and ComD

yellow). The null association rate (dash lines) and jackknife error bars are shown. . 77

Figure 3.4. Lagged association rates (LAR) for males and females (faint colour)

bottlenose dolphins of each community ((a)-ComB, (b)-ComC and (c)-ComD). The

null association rate (dash lines) and jackknife error bars are shown. Note that the

LAR for ComA could not be estimated because of small sample sizes..................... 78

Figure 3.5. Study area showing the bathymetry and core areas (a) based on the 50%

kernel density and home ranges (b) based on the 95% kernel density estimated for

each community using clustered individual sightings. Communities are: ComA-

purple; ComB-red; ComC-green; and ComD-yellow. ............................................... 71

Figure 4.1. Map of the four geographic regions (in bold) associated with each socio-

ecological subpopulation within the Perth metropolitan waters, Western Australia.

.................................................................................................................................. 109

Figure 4.2. Bayesian assignment probabilities from STRUCTURE for bottlenose

dolphins based on ten microsatellite loci: (a) without prior information, K = 3. (b)

With prior information, K = 4. Each vertical line represents one individual, with the

strength of that individual to any of the genetic clusters (blue cluster 1; red cluster 2;

green cluster 3; purple cluster 4). Individuals are grouped by social subpopulations

(GR = Gage Roads, SCR = Swan Canning Riverpark, OA = Owen Anchorage, CS =

Cockburn Sound) and sorted within subpopulations by the latitude of sampling

location from North (left) to South (right) when available. ..................................... 117

Figure 4.3. Median-joining network of mtDNA control region haplotypes in Indo-

Pacific bottlenose dolphins in Perth metropolitan waters. The size of the circles is

proportional to the total number of individuals carrying that haplotype. Different

colours denote the four different sampled subpopulations: purple GR = Gage Roads,

red SCR = Swan Canning Riverpark, green OA = Owen Anchorage and yellow CS =

Cockburn Sound. Number of mutational events between each haplotype is indicated

by hash marks. .......................................................................................................... 121

Figure 5.1. Map of the sampling sites, southwestern Australia, showing the biopsy

sample collection sites for bottlenose dolphins (n = 221) from eight localities: blues

xxii

= Perth (PE) including Swan Canning Riverpark (SCR) and Cockburn Sound

(CS/OA); black = Mandurah (MH); green = Bunbury (BB); orange = Busselton

(BS); purple = Augusta (AU); grey = Albany (AL); and pink = Esperance (ES). .. 141

Figure 5.2. Structure plots showing assignment probability of each dolphin (one

individual per column) to the respective populations for different number of clusters,

K: (a) with K = 2 and (b) K = 4 for the full dataset (n = 221); and (c) with K = 3 for

Augusta, Albany, and Esperance only (n = 53). PE = Perth (including SCR = Swan

Canning Riverpark and CS/OA = Cockburn Sound), MH = Mandurah, BB =

Bunbury, BS = Busselton, AU = Augusta, AL = Albany, ES = Esperance............. 149

Figure 6.1. Summary overview of the conservation status of bottlenose dolphins in

the four geographic regions of Perth metropolitan waters: SCR = Swan Canning

Riverpark (red), CS = Cockburn Sound (yellow), OA = Owen Anchorage (yellow),

GR = Gage Roads (green). ....................................................................................... 181

xxiii

List of Appendices

Appendix A2.1. Survey effort ................................................................................... 38

Appendix A2.2. The CloseTest: a method to investigate of the population closure . 39

Appendix A2.3. Data processing for photograph quality and individual

distinctiveness ............................................................................................................ 40

Appendix A2.4. Method to estimate proportion of distinctly marked individuals and

correction of the marked abundance estimates for consideration of the proportion of

unmarked individuals (D3 individuals identified from Q1 and Q2) .......................... 41

Appendix A2.5. Test for heterogeneity in capture probabilities by implementing

goodness-of-fit tests for multistate models using the program U-CARE .................. 43

Appendix A2.6. Bottlenose dolphin groups .............................................................. 44

Appendix A2.7. Bottlenose dolphin individuals ....................................................... 46

Appendix A2.8. Multistate closed robust design models summary .......................... 13

Appendix A2.9. Sighting frequency .......................................................................... 48

Appendix A2.10. Coefficient of variations (CV) of capture probability (p) ............ 49

Appendix A2.11. MSCRD analysis using sex as an individual covariate ................ 50

Appendix A3.1. Social dendrogram .......................................................................... 94

Appendix A3.2. Tests of differences in distribution of centrality values between

communities ............................................................................................................... 95

Appendix A3.3. Network parameters ........................................................................ 96

Appendix A3.4. Best-fitted models for the lagged association rate (LAR) .............. 55

Appendix A3.5. Core areas and home ranges ......................................................... 100

Appendix 3.6. Bathymetry ...................................................................................... 101

Appendix A3.7. Overlap between social communities ........................................... 102

Appendix A3.8. Best-fitted models for the lagged identification rate (LIR) ............ 55

Appendix A4.1. Linkage disequilibrium ................................................................. 129

Appendix A4.2. Mean of the posterior probabilities (LnP(D)) and ΔK statistic for

STRUCTURE .......................................................................................................... 130

Appendix A4.3. Mean assignment indices (AIc) .................................................... 131

Appendix A4.4. Sex-biased dispersal ..................................................................... 132

Appendix A4.5. Distribution of allelic frequencies ................................................ 133

Appendix A5.1. Genetic diversity for 25 microsatellite loci .................................. 162

Appendix A5.2. Bottleneck tests ............................................................................. 167

xxiv

Appendix A5.3. Isolation by distance ..................................................................... 168

Appendix A5.4. Mean of the posterior probabilities (LnP(D)) and ΔK statistic for

STRUCTURE .......................................................................................................... 170

Appendix A5.5. Correlation between sample size n and estimated effective

population size (Ne and cNe)..................................................................................... 171

Appendix A5.6. Abundances estimated for dolphins using the Perth metropolitan

waters using single state Closed Robust Design models ......................................... 172

xxv

Glossary

The terms defined in the glossary will be highlighted by a dagger (†) when first used

in the text:

Term Definition

Allele Variant form of a given gene (Taylor et al. 2010).

Allele richness Measure of the number of alleles that takes into account

variations in sample size (Greenbaum et al. 2014).

Bottleneck Drastic reduction of the effective population size† of a

population (Slatkin 2008).

Effective population

size

Number of breeders in an idealised population (in Hardy-

Weinberg equilibrium†) that would show the same amount of

genetic drift† or inbreeding or accumulation of linkage

disequilibrium† as the population under consideration (Taylor

et al. 2010).

Genetic drift Random variation of allele frequencies in transmission

between generations (Taylor et al. 2010; Nielsen and Slatkin

2013).

Haplotype Group of genes in an organism that are inherited together

from a single parent (Nei 1987).

Haplotypic diversity Probability that two randomly sampled alleles or haplotypes†

are different (Nei 1987).

Hardy-Weinberg

equilibrium

Principle stating that allele and genotype frequencies in a

population remain constant from generation to generation in

the absence of other evolutionary influences (Nei 1987).

Linkage

disequilibrium

Non-random association of alleles at different loci (Slatkin

2008).

Nucleotide diversity Average number of nucleotide differences per site in pairwise

comparisons among DNA sequences (Nei and Li 1979).

Null allele Allele (at a microsatellite locus) which is present in a sample

but which consistently fails to amplify during polymerase

chain reaction (PCR). Amplification of the allele can be

inhibited because of a mutation in the primer binding region

(Chapuis and Estoup 2007).

Private alleles Alleles that are only found in one population (Szpiech and

Rosenberg 2011).

xxvi

List of abbreviations and symbols

Abbreviation

Symbol

Meaning

θ Proportion of distinctly marked individuals

π Nucleotide diversity

φ Survival rate

ψ Movement transition

φST Nucleotide differentiation

AIc Assignment index correction

AICc Akaike information criterion

AR Allele richness

c Probability of recapture

CCC Cophenetic coefficient correlation

CI Confidence interval

CRD Closed robust design

CS Cockburn Sound

CV Coefficient of variation

DMSO Dimethyl sulfoxide

EIA Environmental impact assessment

FIS Inbreeding coefficient

FST Fixation index (genetic distance/differentiation)

GOF Goodness-of-fit

GR Gage Roads

h Haplotypic diversity

HO Observed heterozygosity

HE Expected heterozygosity

hrs Hours

HWE Hardy-Weinberg equilibrium

HWI Half-weight index

IBD Isolation by distance

KDE Kernel density

km Kilometre

LAR Lagged association index

LIR Lagged identification index

xxvii

m Migration

M Modularity

MCMC Markov chain Monte Carlo

MR Mark-recapture

MS Multistate mark-recapture

MSCRD Multistate closed robust design

mtDNA mitochondrial DNA

n Sample size

N or Nc Abundance -- Number of individuals in the study area (Chapters 2

and 3) or Census population size in genetic context (Chapters 4 and

5)

NA Number of alleles

Ne Effective population size

NM Nautical mile

OA Owen Anchorage

p Probability of capture

PA Private alleles

QG Queller and Goodnight index

R Coefficient of correlation

r Frequency of null alleles

S Social differentiation

SCR Swan Canning Riverpark

SE Standard error

SHUA Shannon’s mutual information index

SMM Stepwise mutation model

td Time lag

TPM Two-phase model

WA Western Australia

xxviii

1

Chapter 1. General Introduction

1.1. Characterising the population structure of small cetaceans in coastal and

estuarine environments

Coastal and estuarine ecosystems adjacent to urban centres are challenging

environments for small cetaceans (Reeves et al. 2003; Jefferson et al. 2009;

Cagnazzi et al. 2013a; Derville et al. 2016). Small cetaceans inhabiting these areas

may experience: habitat loss and degradation (Ross 2006; Culloch et al. 2016);

incidental mortality from indirect and direct interactions with commercial and

recreational fisheries (Dawson and Slooten 1993; Curry and Smith 1997; Slooten et

al. 2000; Chilvers et al. 2003; Reeves et al. 2003); disturbance and harassment from

vessel interactions and anthropogenic noise (Donaldson et al. 2010; Pirotta et al.

2013; Christiansen et al. 2016; Culloch et al. 2016; Marley et al. 2016); and

exposure to environmental contaminants (Todd et al. 2015). These stressors can

affect the behaviour, physiology, and health of cetaceans and reduce reproductive

success and survival, particularly if stressors exert cumulative or synergistic impacts

(Bejder et al. 2006; Van Bressem et al. 2009; Jefferson et al. 2009; Christiansen and

Lusseau 2015). Given the complex and heterogeneous distribution of small cetaceans

in these habitats, it can be challenging to identify and implement appropriate

conservation measures. Therefore, it is vital to improve assessments of the

population status, the biological significance of human impacts, and the effectiveness

of management approaches (Taylor 2005; Bejder et al. 2009; Berger-Tal et al. 2011).

In particular, the methods and theoretical frameworks used to characterise their

population structures should be able to identify the appropriate putative ‘units to

conserve’ (sensu Taylor 2005).

The heterogeneity of coastal and estuarine environments means that, rather than

there being a single continuously distributed population or species, small cetaceans

are often distributed as multiple, localised populations or subpopulations (e.g.,

Brown et al. 2016). Local- or sub-populations are often defined by their association

with particular areas and are linked, in varying degrees, to each other, but often have

unique characteristics (demographic, ecological or genetic) that make them


2

somewhat distinctive (e.g., Curry and Smith 1997; Sellas et al. 2005; Möller et al.

2007). A subpopulation can be characterised as having: 1) distinctive ecological

characteristics (e.g., use of particular prey, foraging tactics, or habitats); 2) strong

associations between particular individuals; and 3) natal philopatry (i.e., retention of

maternal home range, Rossbach and Herzing 1999; Wells et al. 1999; Connor et al.

2000b). Wells et al. (1999) used the term ‘community’ to define a ‘regional society

of animals sharing ranges and associates’. Subpopulation and community definitions

overlap to some extent, but a community should still exhibit genetic exchange with

other similar units.

While there is much enthusiasm for identifying subdivisions within a species’ range,

humans’ perceptions of such subdivisions are often not very biologically meaningful

(Taylor, 2002). Nonetheless, it is better to consider subdivisions as humans perceive

them, and estimate dispersal between these subdivisions, which is the most useful

information for both evolutionary biology and management. This thesis is based on

the premise that, in some circumstances, a subpopulation will be an appropriate ‘unit

to conserve’. Rather than engaging in an exhaustive discussion of the policy or

statutory bases for how management units or ‘units to conserve’ are to be defined, I

intend to provide – in the context of a major metropolitan area with multiple

anthropogenic stressors present – an empirical framework for the population

structure of small cetaceans in an urbanised region. I then discuss potential ‘units to

conserve’ in that region, based on the evidence presented and available information

on human environmental impacts.

Small cetaceans are long-lived species characterised by slow growth rates, late

maturation, and low reproductive rates (Taylor 2002; LeDuc 2009). As a

consequence, the persistence of populations depends on their ability to sustain a net

positive reproductive output (Caughley 1977). Thus, human activities that either

singly or cumulatively affect the health of individuals in a population by repeatedly

disturbing ecologically vital processes (e.g., resting, foraging, and reproducing,

Lusseau 2003a; Bejder et al. 2006; Tyne et al. 2014) or through direct mortality

(e.g., incidental capture in fisheries and boat strikes, Stone and Yoshinaga 2000;

Lewison et al. 2004; Baker et al. 2013) may jeopardise the population’s viability.


3

The level and intensity of human activities vary across regions and habitats, and each

subpopulation (which may have differing demographic, ecological and genetic

characteristics) has varying levels of exposure to human impacts. Therefore, the

identification of ‘units to conserve’ in a population is a fundamental aspect of impact

assessment (Taylor 2005), but not currently required under Australian wildlife

protection law. In contrast, the United States Marine Mammal Protect Act 1972

specifically requires the identification of marine mammal ‘stocks’ for management.

The scientific argument for considering subpopulations as ‘units to conserve’ is

based on evidence that subpopulations: (1) are demographically independent from

populations in neighbouring areas; (2) maintain unique associations with a particular

geographic area or ecosystem; (3) are genetically differentiated; and (4) possess

unique cultural traditions (Rendell and Whitehead 2001; Sellas et al. 2005; Taylor

2005; Möller et al. 2007; Fury and Harrison 2008; Urian et al. 2009; Wiszniewski et

al. 2009).

Individual-based behavioural and genetic parameters are typically used to

characterise the population structure of small cetaceans (Figure 1.1) (e.g., Lusseau

and Newman 2004; Tezanos-Pinto et al. 2009; Urian et al. 2009; Wiszniewski et al.

2009; Titcomb et al. 2015; Thompson et al. 2016). An analysis of residency patterns

examines how individuals occupy an area over time, allowing for discrimination

between animals present only for certain periods and those inhabiting an area

continuously (i.e., site fidelity) (Whitehead 2001; Zolman 2002; Chilvers and

Corkeron 2003; Chabanne et al. 2012). Mark-recapture models can estimate

abundance and apparent survival that define the probability of surviving and staying

within a region and can also be interpreted as a measure of residency (Smith et al.

2013; Brown et al. 2016). Analyses of association patterns between individuals can

characterise the social structure within an area, including the existence of distinct

groupings (Whitehead 1995; Urian et al. 2009; Wiszniewski et al. 2009; Oudejans et

al. 2015; Titcomb et al. 2015). Analyses of the kernel density index (KDE) can

describe the home range of subpopulations and can be used to infer the degree of

spatial distinctiveness (i.e., site fidelity that can be associated with use of particular

prey or foraging tactics) (Wiszniewski et al. 2009; Sprogis et al. 2015; Titcomb et al.

2015). Analyses of genetic markers can be used to describe whether or not habitat

boundaries and residency in sheltered ecosystems can also promote genetic


4

differentiation, genetic relatedness or lack of dispersal (i.e., genetic structure)

between groups of cetaceans ranging over relatively small geographical distances

(Möller et al. 2007; Andrews et al. 2010).

Below, I discuss three practical approaches (see Figure 1.1) and considerations that

are relevant to the methods applied in this study to characterise the local dolphin

population structure in and around Perth metropolitan waters and to identify

potential ‘units to conserve’.

Study design for systematic surveys

The study of small cetaceans in coastal and estuarine environments requires designs

that allow for the systematic collection of individual-specific spatial and social data

(Figure 1.1; see Morrison et al. 2008a). Consideration of several practical issues

must be made, and should include:

Deciding on the study area and spatial scale(s) of interest (i.e., the overall

seascape and particular sub-areas);

Investigating a range of variables, such as the environmental features (e.g.,

salinity, temperature, and bathymetry) used to delineate sub-areas/habitats in

coastal and estuarine ecosystems. While stratification of a study area into blocks

for sampling purposes is generally related to logistical considerations, it can have

specific research applications (e.g., to generate abundance estimates for different

sub-areas/habitats) and may increase the precision of various estimated

parameters (Morrison et al. 2008b; Hammond 2010);

Applying a realistic timeframe for sampling (i.e., knowledge of weather

conditions in the study area and available daylight hours when designing survey

transects); and,

Balancing the study area coverage with field survey time and budget. A survey

design should aim for equal coverage probability of the study area (including

between blocks or sub-areas). In practice, a design involving a set of zig-zag

transect lines has been found to be the most appropriate for boat-based surveys of

small cetaceans, because it allows for relatively even coverage and minimises

time spent transiting between transects (Hammond 2010).


5

Photo-identification

Photo-identification (Figure 1.1) techniques were first applied to small cetaceans in

the early 1970s and are now an integral component of many field studies (Würsig

and Jefferson 1990). The objective of this technique is to recognise individuals by

using natural marks (or those from anthropogenic sources such as boat strikes and

entanglements) recorded in photographs. There are practical challenges to obtaining

photographs of good quality and protocols to ensure appropriate sampling of well-

marked, as well as un-marked (‘clean fin’) individuals, must be carefully considered

(Urian et al. 2014). Nonetheless, photo-identification has proved to be one of the

most useful approaches for understanding small cetacean life history (Hammond et

al. 1990) and for mark-recapture studies that assess abundance and apparent survival

(e.g., Nicholson et al. 2012; Ansmann et al. 2013; Brown et al. 2016; Sprogis et al.

2016). When combined with individual spatial and temporal information, photo-

identification can be used to identify site fidelity, movement patterns and home

range size, as well as social structure (e.g., Chabanne et al. 2012; Oudejans et al.

2015; Sprogis et al. 2015; Titcomb et al. 2015).

Biopsy sampling

Remote biopsy sampling (Figure 1.1), using either a crossbow or modified rifle to

propel a small biopsy dart, is now in widespread used for obtaining tissue samples

from free-ranging cetaceans (Krützen et al. 2002). Biopsy darting has facilitated

genetic analyses to determine the sex of individuals, which is important for

monitoring population dynamics (e.g., Frère et al. 2010b; Sprogis et al. 2016).

Recognising sex-specific ecological differences (e.g., in ranging patterns) within a

population could indicate issues for conservation and management, as one sex may

be more susceptible to anthropogenic threats than the other (e.g., Lusseau 2003b;

Stensland and Berggren 2007; Sprogis et al. 2015). Secondly, techniques for the use

of genetic markers (e.g., microsatellite loci and mitochondrial DNA) are now well-

advanced (Frankham 1995a; Wan et al. 2004) and are able to support analyses to

investigate the presence of inbreeding depression, population genetic structure, gene

flow, effective population size, and evolutionary history, all of which are critical

forms of information for understanding the viability of putative ‘units to conserve’

within a seascape.


6

Inferring demographic or genetic isolation

With adequately designed surveys, photo-identification can be used to determine

whether populations living in different geographic areas are demographically

isolated or not (e.g. Gaspari et al. 2015; Brown et al. 2016), and this can be

corroborated with the integration of genetic data (e.g., Andrews et al. 2010).


7

Figure 1.1. Practical approaches (with considerations) and scientific information used to identify putative ‘units to conserve’ (subpopulations or

local populations) in a population of a small cetacean species.


8

1.2. The Indo-Pacific bottlenose dolphin

The Indo-Pacific bottlenose dolphin Tursiops aduncus (Ehrenberg 1832) was

recognised as a separate species from the common bottlenose dolphin Tursiops

truncatus in the late 1990s, based on differences in genetics, osteology, and external

morphology (LeDuc et al. 1999; Wang et al. 1999, 2000a, b). T. aduncus has a wide

distribution in the warm temperate to tropical coastal and shallow waters of the

Indian Ocean, Indo-Pacific region and western Pacific Oceans, including South

Africa in the west, the Red Sea, Persian Gulf and Indo-Malay Archipelago along the

rim of the Indian Ocean, to the southern half of Japan, and southward to Australia

along both east and west coasts (Rice 1998; Hale et al. 2000; Möller and

Beheregaray 2001; Reeves et al. 2003; Wang and Yang 2009; Gibbs et al. 2011;

Allen et al. 2016). Along the north-western coastline of Western Australia, both T.

truncatus and T. aduncus are documented, with the former found in the offshore,

pelagic environment and the latter found in waters < 50 m deep and within

approximately 10 km of the coastline (Allen et al. 2016).

T. aduncus is distributed across a wide range of habitats throughout temperate and

tropical waters. In Australia, T. aduncus is restricted to inshore areas, such as bays

and estuaries, nearshore waters, open coastal environments, and shallow offshore

waters including coastal areas around reefs and oceanic islands (e.g., Hale 1997;

Bilgmann et al. 2007; Fury and Harrison 2008; Wiszniewski et al. 2010; Chabanne

et al. 2012; Sprogis et al. 2015; Allen et al. 2016). In south-eastern Australia, for

example, T. aduncus shows a high degree of site fidelity to local areas and appears to

reside in relatively small communities or populations (Möller et al. 2002). Despite

the potential for long-distance movements within their broad distribution, significant

genetic differentiation has been detected both within ocean basins and on a

microgeographic scale within localised study sites (e.g., Ansmann et al. 2012; Kopps

et al. 2014).

The nearshore distribution of T. aduncus makes them easily accessible, resulting in

them being well-known and extensively studied cetacean species (Connor et al.

2000b). The species is not considered threatened in Australian waters, although T.

aduncus is still classified as ‘Data deficient’ in the most recent International Union


9

for Conservation and Natural Resources (IUCN) species classification (Hammond et

al. 2012). Populations of T. aduncus are exposed to a wide variety of threats that are

technically challenging to quantify in terms of their precise impact, but which

cumulatively could cause long-term population declines (Manlik et al. 2016). Some

of the main threats likely to affect populations of T. aduncus include: habitat

degradation and destruction (Ross 2006); pollution and coastal development (Steiner

and Bossley 2008); entanglements and boat strikes (e.g., Steiner and Bossley 2008;

Donaldson et al. 2010).

1.3. Indo-Pacific bottlenose dolphin population in Perth metropolitan waters

Perth is located in south-west Australia (S31.93, E115.86). The Perth metropolitan

waters (as defined for this thesis) encompass approximately 300 km2

of coastal and

estuarine ecosystems from southern Cockburn Sound (off Rockingham township) to

the reef at Trigg Beach, including the Swan Canning Riverpark (Figure 1.2). The

greater Perth metropolitan region continues to the north and south of the study area.

Several independent studies were conducted in the Perth metropolitan region in the

last three decades, providing some knowledge of the ecology of T. aduncus

associated with different areas and habitats (Waples 1997; Finn 2005; Moiler 2008;

Chabanne et al. 2012):

Area 1 (Figure 1.2): Waples (1997) encountered more than 270 free-ranging

bottlenose dolphins during boat-based surveys conducted from 1991 to 1993 and

across a large area within the Perth metropolitan region (i.e., from the north of

Cockburn Sound to the south of Lancelin, including Rottnest Island). Waples (1997)

suggested that bottlenose dolphins were present year-round with evidence of

different patterns of residency and associations (i.e., some repeated associations and

many casual affiliates).

Area 2 (Figure 1.2): In Cockburn Sound, photo-identification studies of bottlenose

dolphins were conducted during three periods: 1993-1997 (R. Donaldson, Murdoch

University, unpublished data), 2000-2003 (Finn 2005) and a short investigation in

2009 (Ham 2009). Since there are few embayments along the western coastline of

http://tools.wmflabs.org/geohack/geohack.php?pagename=Perth&params=31_57_8_S_115_51_32_E_type:city_region:AU-WA


10

Western Australian, Cockburn Sound is the most intensively utilised coastal area

(Environmental Protection Authority 1998). The proximity of the deep and protected

waters of the Sound to the capital city of Perth led to intensive developments of the

area for industry and maritime operations, including the Outer Harbour for the Port

of Fremantle, the Kwinana Industrial Area, and the Australian Navy base at HMAS

Stirling on Garden Island. In addition, the Sound supports several commercial and

recreational fisheries and aquaculture operations, as well as a range of tourism and

recreational activities (Cockburn Sound Management Council 2012). Donaldson

(unpublished data) and Finn (2005) described a resident subpopulation of c. 75

bottlenose dolphins and the importance of the Kwinana Shelf as a key foraging

habitat. Those studies involved some investigation of conservation issues such as

illegal feeding (i.e., unregulated provisioning), entanglements, vessel disturbance,

and habitat change related to industrial development and human-induced ecosystem

modification affecting the population of bottlenose dolphins in this area (Finn 2005;

Finn and Calver 2008; Finn et al. 2008; Donaldson et al. 2010, 2012a, b). The study

by Ham (2009) suggested that the abundance of dolphins in the area was stable or at

least that no precipitous decline had occurred since the prior investigations by

Donaldson (1993-1997) and Finn (2000-2003).

Area 3 (Figure 1.2): In the Swan Canning Riverpark (SCR), a micro-tidal estuary,

studies of T. aduncus began in late 2001 and continued with intense surveys up to

2003, followed by more sporadic efforts until the start of my PhD study in 2011.

Based on the survey data covering the period from 2000 to 2003, Chabanne et al.

(2012) indicated that the community within the SCR was small (c. 17-18 individuals)

but characterised by a strong residency pattern based on their year-round presence

and their preferential and long-term associations with other individuals within the

SCR.

In 2009, six bottlenose dolphins were found dead within a six-month period in the

SCR (Holyoake et al. 2010; Stephens et al. 2014). The unusual mortality event

raised concerns about the welfare of dolphins inhabiting estuarine environments and

resulted in an investigation supported by the Swan River Trust into the cause of the

deaths. Post-mortem examination of four dolphins (the two other dolphins were too

decomposed) indicated that a suite of factors likely contributed to the mortality of


11

each individual (i.e., skin lesions, active entanglements, secondary infections)

(Holyoake et al. 2010). Stephens et al. (2014) also confirmed the presence of

cetacean morbillivirus in two of the dead dolphins.

Figure 1.2. The three areas previously studied for bottlenose dolphins in the

metropolitan waters of Perth (Western Australia) and the scale of the current study

(red square, 2011-15). Area 1 – Open coastline (more effort in dark purple area than

light purple area, Waples 1997); Area 2 – Cockburn Sound (green, Finn 2005); Area

3 – Swan Canning Riverpark (blue, Chabanne et al. 2012). RI = Rottnest Island;

SCA = Scarborough.


12

1.4. Objectives and structure of the thesis

In this thesis, I aim to provide the scientific basis for decision-making around ‘units

to conserve’ for bottlenose dolphins in the Perth region. There are multiple

imperatives for this research, including the concerns raised by the unusual mortality

event in 2009 (Holyoake et al. 2010), rapid human population growth in the region

(Australian Bureau of Statistics 2015), and proposed coastal developments, for

example, the outer harbour development on the Kwinana Shelf (Western Australian

Planning Commission 2004) and a desalination plant proposed for the northern

metropolitan coast (Mercer 2013). The scientific basis I will provide includes

information about population structure (Chapters 2 and 3, Chabanne et al. 2017a, b),

encompassing genetic population structure at local- (Chapter 4) and regional-scales

(Chapter 5), and information about the size and status of the subpopulations

identified. I also illustrate how such information can be applied to assist in decision-

making about the appropriate "local population" for the purposes of Environmental

Impact Assessment (Chapter 3, Chabanne et al. 2017a).

Specifically, the objectives of my thesis are to:

Chapter 1. Develop a systematic and robust photo-identification sampling design

using multistate mark-recapture models (i.e., Multistate Closed Robust Design,

MSCRD) to estimate the apparent survival, abundance and movement rates of

bottlenose dolphins associated with defined geographic regions (Chapter 2,

Chabanne et al. 2017b);

Chapter 2. Identify local populations of bottlenose dolphins through the

integration of social, ecological and genetic data collected during comprehensive

and consistent sampling effort. Specifically, I assess the social network, examine

the spatial segregation, and evaluate the genetic relatedness of the socio-

geographic bottlenose dolphin communities in the study area. I then demonstrate

the relevance of the fine-scale population structure for Environmental Impact

Assessments (EIA) of coastal developments (Chapter 3, Chabanne et al. 2017a);

Chapter 3. Examine whether the social structure of the bottlenose dolphin

population is reflected through genetic differentiation (i.e., diversity and

structure) using microsatellite loci and mitochondrial DNA markers (Chapter 4);


13

Chapter 4. Investigate the patterns of gene flow between the population in Perth

metropolitan waters and others at a regional-scale (southwestern coastline of

Western Australia) using microsatellite loci markers (Chapter 5); and,

Chapter 5. Summarise key findings and make recommendations to support the

conservation and management of local dolphin populations in Perth metropolitan

waters (Chapter 6).

This thesis has been written in the style of a ‘thesis by publication’, following the

Murdoch University style guideline for thesis by publication/manuscripts. This

chapter (Chapter 1) provides a general introduction and thesis overview. Data

chapters (Chapters 2 to 5) have been prepared as stand-alone papers and are

presented here with minimal changes from the versions submitted or published,

although references are collated across chapters. Finally, in Chapter 6, I synthesise

the main findings relevant to stakeholders and government agencies on the current

status of bottlenose dolphins in Perth metropolitan waters. I also discuss some of the

theoretical and practical limitations of the approaches used in this thesis, and

conclude with recommendations for future management strategies.

1.5. Ethics statement

This study was carried out with approval from the Murdoch University Animal

Ethics Committee (W2342/10 and R2649/14) and was licensed by the Department of

Parks and Wildlife (SF008067, SF008682, SF009286 and SF009874). Biopsy

sampling for molecular analyses were carried out as a part of broader study, with

data collected in accordance with the Murdoch University Animal Ethics Committee

approval (W2076/07; W2307/10; W2342/10 and R2649/14), and collected under

research permits (SF005997; SF006538; SF007046; SF007596; SF008480;

SF009119: SF009734; SF010223) from the Department of Parks and Wildlife.

15

Chapter 2. Applying the multistate capture-recapture robust design

to assess metapopulation structure

2.1. Abstract

1. Population structure must be considered when developing mark–recapture (MR)

study designs as the sampling of individuals from multiple populations (or

subpopulations) may increase heterogeneity in individual capture probability.

Conversely, the use of an appropriate MR study design which accommodates

heterogeneity associated with capture occasion varying covariates due to animals

moving between ‘states’ (i.e., geographic sites) can provide insight into how animals

are distributed in a particular environment and the status and connectivity of

subpopulations.

2. The multistate closed robust design (MSCRD) was chosen to investigate: (i) the

demographic parameters of Indo-Pacific bottlenose dolphin (Tursiops aduncus)

subpopulations in coastal and estuarine waters of Perth, Western Australia; and (ii)

how they are related to each other in a metapopulation. Using four years of year-

round photo-identification surveys across three geographic sites, I accounted for

heterogeneity of capture probability based on how individuals distributed themselves

across geographic sites and characterised the status of subpopulations based on their

abundance, survival and interconnection.

3. MSCRD models highlighted high heterogeneity in capture probabilities and

demographic parameters between sites. High capture probabilities, high survival and

constant abundances described a subpopulation with high fidelity in an estuary. In

contrast, low captures, permanent and temporary emigration and fluctuating

abundances suggested transient use and low fidelity in an open coastline site.

4. Estimates of transition probabilities also varied between sites, with estuarine

dolphins visiting sheltered coastal embayments more regularly than coastal dolphins

visited the estuary, highlighting some dynamics within the metapopulation.

5. Synthesis and applications. To date, bottlenose dolphin studies using mark-

recapture approach have focussed on investigating single subpopulations. Here, in a

heterogeneous coastal-estuarine environment, I demonstrated that spatially structured

bottlenose dolphins subpopulations contained distinct suites of individuals and

Chapter 2 - Population structure investigated by MSCRD

16

differed in size, demographics and connectivity. Such insights into the dynamics of a

metapopulation can assist in local-scale species conservation. The MSCRD approach

is applicable to species/populations consisting of recognizable individuals and is

particularly useful for characterising wildlife subpopulations that vary in their

vulnerability to human activities, climate change or invasive species.


17

2.2. Introduction

At an individual level, wildlife tends to be neither uniformly nor randomly

distributed across land- or sea-scapes but to occur in association with particular

environmental features (Legendre and Fortin 1989). At a population-level, species

are typically distributed in a series of populations or ‘subpopulations’, as in a

metapopulation model (i.e., set of spatially separated populations of the same species

which interact at some level, Levins 1969). Emigration and immigration between

subpopulations may occur through either permanent additions or subtractions or only

the short-term presence or absence of individuals (Brown et al. 2016; Sprogis et al.

2016). Individuals within a population (or subpopulation) may have ranging patterns

that overlap or are connected with a particular locality (Sprogis et al. 2015).

Such population structure must be considered when developing mark-recapture

(MR) study designs because the sampling of individuals from multiple populations

(or subpopulations) may increase heterogeneity in individual capture probability

(Brown et al. 2016). Conversely, it is feasible for an appropriate MR study design

also to provide insight into how animals are distributed in a particular environment

and the status and connectivity of any subpopulations that are present (Brooks and

Pollock 2014).

Since its development in the late 1800s (Petersen 1895), the MR approach has been

widely used for assessing wildlife abundance, distribution and demographic

processes. Here, I attempted to use extensions of a MR study design, the multistate

closed robust design (MSCRD), to investigate demographic parameters and

connectivity between putative subpopulations that were spatially predefined in a

heterogeneous coastal and estuarine environment.

In MR studies, individual-specific encounter (‘capture’) histories may be used to

generate capture probabilities, and to estimate apparent survival rates (i.e., the true

survival and permanent emigration combined) and abundance (i.e., number of

animals in the study area, Lettink and Armstrong 2003). The underlying assumption

of homogeneity in individual capture probabilities is often violated because of

practical constraints on sampling (see review by Lindberg 2012). Heterogeneity in


18

individual capture probability may be reduced by the inclusion of time-dependent

covariates (e.g., year), individual time-constant covariates (e.g., sex), and covariates

associated with individual capture occasion (e.g., weight, social affiliations,

geographical locations: Pollock et al. 1990).

The closed robust design (CRD) was built using two different temporal scales: (i)

two or more open sampling occasions (hereafter ‘primary periods’) in which the time

interval between periods is sufficiently long enough to allow for births and

immigration, and for losses from deaths and emigration; and, (ii) closed sampling

occasions (hereafter ‘secondary occasions’) set within each of the primary periods

and where the intervals between occasions are sufficiently short so that no gains and

losses are assumed to occur (Pollock 1982). By sampling across multiple temporal

scales, CRD models estimate temporary emigration (TE) and immigration between

primary periods as well as abundance and apparent survival parameters without

having to assume equal probability of capture over the entire study period (Kendall

and Pollock 1992; Smith et al. 2013). Thus, biases due to heterogeneity in capture

probability are minimised and abundance and apparent survival are estimated from

multiple occasions allowing better precision (Kendall 1990). The incorporation of

time-constant covariates (e.g., sex) within the CRD models also has advantages in

reducing the heterogeneity in capture probability and estimating abundance and

apparent survival specific to covariate classes (e.g., males, females).

Another MR study design, the multistate mark-recapture (MS) approach, enables the

use of fixed set of categorical ‘states’ that are discrete covariates measured upon

capture of the individual, e.g., geographic location, reproductive state (e.g., Hestbeck

et al. 1991; Cam et al. 2004). Like time-constant covariates, an advantage of

including categorical ‘states’ in MS models is a homogeneity assumption that is

state-specific and the ability of the models to provide state-specific estimates for

abundance and apparent survival (Lindberg 2012). As well as modelling immigration

and emigration to and from an unobservable state (i.e., outside the study area, and

thus part of the apparent survival estimates), MS models have a unique feature in

which transition among ‘states’ can be estimated (Darroch 1961; Arnason 1972,

1973). The transition between states is the probability that an individual, alive and in

the state x, just before t+1, emigrates into the state y. Transition between states may


19

be either temporary or permanent and both contribute to the estimate of transition

probability.

Here, I applied the MSCRD approach with ‘states’ referring to geographic sites (see

‘site’ hereafter), which utilizes aspects of MS models and the CRD (Nichols and

Coffman 1999) (Figure 2.1) for several reasons. Firstly, the MSCRD allows for

greater flexibility in model specifications for individual heterogeneity in capture

probability. Critically, heterogeneity can be modelled according to: (i) individual-

level characteristics (i.e., a time-constant covariate such as sex), (ii) individual-level

responses to capture (i.e., state measured upon capture) or (iii) the relevant temporal

scale for captures (primary periods vs. secondary occasions). Secondly, the MSCRD

can provide abundance estimates for each ‘state’ within each primary period. Finally,

the inclusion of multiple secondary occasions within each primary period increases

the capture probability, which improves the precision of the apparent survival

estimates and transition probabilities (White et al. 2006; Lindberg 2012).


20

Figure 2.1. Traditional closed robust design (CRD) vs. multistate closed robust

design (MSCRD) approaches to characterise metapopulation structure and dynamics

through demographic parameters. Both approaches allow estimation of abundance

(N), apparent survival rate (φ) and emigration and immigration [solid arrows] either

time varying (t, t+1, t+2, etc.) or constant. In addition, MSCRD models estimate any

transition probabilities ψ [dashed arrows] between subpopulations associated with

states (i.e., geographic sites).


21

My aim is to show how the MSCRD, with its innate flexibility in modelling

heterogeneity in capture probabilities, can simultaneously provide demographic

parameter estimates for multiple putative subpopulations associated with particular

sites as well as describe their conservation status and connectivity to other

subpopulations. This approach allows for use of: (i) capture probabilities to affirm

(or refute) the putative grouping of individuals associated with a particular site as a

distinct ‘subpopulation’ (i.e., homogeneity within sites); (ii) estimates of the

variation in abundance (i.e., primary period changes in the number of individuals in

any geographic site) and apparent survival (i.e., the probability of surviving and

staying in any site) to assess the occupancy (or residency) of a group of individuals

in that site; and (iii) transition probabilities between sites to describe the

interconnectivity of those groupings.

Previous MSCRD studies using site as a ‘state’ have generally had other aims and

applications: e.g., detecting changes in transition probabilities before, during and

after an environmental perturbation affecting one state (see O'Connell-Goode et al.

2014) or human development activities (see Brooks and Pollock 2014), evaluating

individual fitness over time (see Gibson et al. 2014) or quantifying the connectivity

(i.e., transition of individuals) between areas exposed to different management

regimes (see Lee 2015). Notably, this study aimed to examine the dynamics, status

and connectivity of multiple putative subpopulations each associated with a

particular site.

To pursue the above aim, I applied the MSCRD approach in a mark-recapture study

of Indo-Pacific bottlenose dolphins Tursiops aduncus (‘dolphin’ hereafter) in coastal

and estuarine waters near Perth, Western Australia. I defined the geographic sites

based on the coastal geography and landforms and the known presence of small

resident subpopulations in an estuary (N ≈ 20, Chabanne et al. 2012) and a nearby

coastal embayment (N ≈ 75, Finn 2005) (but without knowledge of their connectivity

to each other, or to other potential subpopulations in the study area).

I then used individual capture histories obtained from four years of year-round boat-

based photo-identification surveys to estimate: (i) capture probabilities per site to

evaluate and compare the occupancy pattern of dolphins within sites (i.e., to explore


22

heterogeneity between sites); (ii) apparent survival rates and abundances as to verify

putative site-related groupings through the assessment of site fidelity (i.e., close to

true survival, stable abundances) and (iii) transition probabilities so as to characterise

movement between site-related groupings (i.e., the metapopulation dynamic).

2.3. Materials and methods

2.3.1. Field methods

2.3.1.1. Study area and field sampling design

My study area encompassed an area of 275 km2, extending for 45 km along the coast

of Perth and then inland to include the Swan Canning Riverpark (SCR), an estuarine

reserve of about 55 km2 (Figure 2.2). Three sites were defined based on coastal

geography, principal landforms (estuary, open waters and coastal embayment) and

information from previous local studies (Waples 1997; Finn 2005; Chabanne et al.

2012): (i) the estuary (SCR) and two sites in coastal waters, (ii) Gage Roads (GR), a

length of open coastline with mostly sandy beaches and small areas of rocky reef and

seagrass, and (iii) Cockburn Sound/Owen Anchorage (CS/OA), a semi-enclosed

embayment. The northern section of the embayment (OA) is of < 10 m depth, except

in a shipping channel (max depth: 14.7 m), with substrates mainly of shell-sand and

seagrass. The southern section (CS) has shallow (< 10 m) margins, a deep (c. 20 m)

central basin, and seagrass, sand, silt and limestone substrates. In comparison to GR,

CS/OA experiences intensive industrial and recreational use, with threats to dolphins

including entanglement and illegal feeding (Finn 2005; Donaldson et al. 2010),

industrial and harbour development (Finn 2005) and shell-sand dredging (BMT

Oceania 2014). For practical reasons (i.e., wind and sea conditions), CS and OA

were split and run as two separate sub-sites, although there were jointly sampled in

84% of the secondary occasions (see below).

Between June 2011 and May 2015, I collected year-round mark-recapture data for

individual dolphins using boat-based photo-identification surveys following pre-

defined transect routes (Figure 2.2). While the same transect route was conducted in

the estuary (due to the confined waters), I rotated between three pre-defined zig-zag


23

transect routes (off-set by 2 km) in the coastal sites to increase sampling coverage

(Figure 2.2). Transect routes were designed using Distance 6.0 (Thomas et al. 2009).

In the robust design language, our primary periods corresponded to the four seasons

in the Australasian calendar: winter (June to August); spring (September to

November); summer (December to February); and autumn (March to May). For this

study, I aimed to conduct at least five secondary occasions (i.e., consecutive surveys

of the three sites) per primary period (n = 16); however, this was not successful for

four primary periods because of weather conditions (Appendix A2.1). If a survey

was interrupted because of weather conditions or logistical issues, the survey was

cancelled and entirely re-run. Surveys of each site were conducted in random order

and at different times of the day.

To limit violation of the closure assumption of a robust design (Pollock 1982;

Nichols and Kendall 1995), I aimed to complete a secondary occasion in the shortest

possible time (i.e., on consecutive days, mean = 2.60; min = 2; max = 8 days,

Appendix A2.1) so as to minimise transitions of the animals (Pollock 1982). When

multiple captures occurred for an individual in a secondary occasion, I retained only

the first capture for that secondary occasion. I then waited for at least one week

(unless weather conditions were excellent and/or I was approaching the end of the

season – primary period) before starting another secondary occasion. The break

between two secondary occasions was longer than the time needed to successfully

complete a secondary occasion (mean = 8.63; min = 0, max = 60 days, Appendix

A2.1), thus allowing us to assume independence between secondary occasions. I also

left a longer interval between two adjoining primary periods (mean = 47.30; min =

12; max = 80 days, Appendix A2.1) to minimise violation of the assumption between

closed and open sampling occasions (Kendall 2004; Brown et al. 2016). The

assumption of closure within primary periods was tested with the program CloseTest

(Stanley and Burnham 1999, see Appendix A2.2 for explanations).


24

Figure 2.2. Map of the metropolitan waters of Perth, Western Australia, showing the

systematic survey routes within each site: the estuary SCR = Swan Canning

Riverpark and the coastal sites (south to north) CS/OA = Cockburn Sound/Owen

Anchorage, and GR = Gage Roads. Within the coastal sites, surveys were conducted

by rotating between three pre-defined transect routes (full, long dash and short dash

lines) to maximise the coverage.

2.3.1.2. Data collection and data processing

To minimise heterogeneity of individual capture probabilities, the vessel was driven

at a constant speed (8-12 knots) with at least three observers on-board to maximise

the area coverage. However, 3% (10 of 304 surveys) of the surveys were conducted

with two observers only. Surveys were conducted in Beaufort sea state ≤ 3. When a

dolphin group was encountered along a transect route, I paused the search effort and

photographed the dorsal fin of each individual on both sides (if possible) and without


25

regard to the distinctiveness of fins. Photographic effort was conducted by the same

person (DBHC) throughout the entire study period. In this study, dolphins were

assigned to the same group when seen within approximately 100 m from the boat

(Wells et al. 1987; Quintana-Rizzo and Wells 2001) and performing similar

activities. Once all dolphins were photographed, the search effort was resumed from

where I had departed the transect route. Photographs of each dolphin group were

then graded for quality by one to three trained assistants and checked by DBHC for

the entire study period. Measures of the quality and individual distinctiveness were

done using modified methods developed by Urian et al. (1999, see Appendix A2.3).

Each individual was assigned a grade for distinctiveness of their dorsal fins to

minimise misidentification and heterogeneity in capture probabilities (Nicholson et

al. 2012).

To minimise heterogeneity in captures due to misidentification of non-distinctive

fins (D3), only individuals with distinctive fins (D1 and D2, Appendix A2.3) were

used in the MSCRD models. Abundance estimates were then adjusted to take into

account the proportion of individuals in the population that were unmarked (D3)

following the method described in Nicholson et al. (2012) (see Appendix A2.4 for

calculation of the proportion of distinctly marked individuals). I also attempted to

address individual heterogeneity in capture probabilities by including sex as an

individual covariate (i.e., female, male or unknown). However, given that 50% of the

individuals were not sexed (n = 169), I acknowledge that estimates obtained through

MSCRD models may be overestimated for sexed individuals and underestimated for

not sexed individuals (Nichols et al. 2004) and for that reason are not presented here

(but see Appendix A2.11 for MSCRD analyses including sex as individual

covariate). Calves, typically less than four years of age (Mann and Smuts 1998),

were excluded from the analysis because of their dependence on their mothers (i.e.,

captures must be independent, Pollock et al. 1990). Heterogeneity in capture

probabilities was tested by implementing goodness-of-fit tests for multistate models

using the program U-CARE (see Appendix A2.5, Pradel et al. 2005; Choquet et al.

2009).


26

2.3.2. Statistical methods

The multistate closed robust design models were run in MARK (White and Burnham

1999) and estimated four parameters per site: (i) abundance (N), which is the number

of individuals present in the study area; (ii) apparent survival rate (φ), which is the

probability of surviving and staying in a sample site; (iii) transition probability (ψ),

which represents the probability of moving from one site to another; and, (iv) capture

probability (p). Although transitions from and to the study area may have occurred

(i.e., TE to an unobservable site), models with an unobservable site never reached

convergence, and thus are not presented. The modelling approach assumes that no

site transitions occurred within a primary period (Arnason 1972, 1973). However, I

acknowledge that 2.6% of the captures violated this assumption. Two adjustments

were made to minimise this violation. First, if an individual was captured in two

different sites within a primary period, I retained captures matching the site of the

first capture recorded in that primary period. Results were similar if the last capture

was retained instead and therefore are not presented here. Second, I ran the MSCRD

models for two different scenarios, including one that involved pooling sites so that

transitions between sites were minimised. Scenario 1 represented the three sites as

originally described in this study area and Scenario 2 had all of the coastal sites

(CS/OA and GR) pooled together into a single Coastal site for comparison with the

estuary (SCR).

In MARK, each MSCRD model combination was run with the probability of capture

(p) varying by site and/or primary period or constant, and with recapture probability

(c) set as equal to first capture probability (p). The abundance (N) was set to vary by

site and primary periods [N(site × primary periods)]. Several sub-models for

apparent survival (φ) were run (i.e., whether it varied by site and/or primary period

or constant). Transition probability between sites (ψ) was also estimated, whether

that parameter varied by site and/or primary period or if it did not vary. In MARK,

time intervals between primary periods were specified as a fraction of a year (i.e.,

0.25) to estimate annual apparent survival and annual transition rates when modelled

as time-constant.


27

Models were ranked using the Akaike information criterion (AICc, Burnham and

Anderson 2002). The model with most support by AICc (highest AICc weight) was

selected as the most parsimonious model. Models with ΔAICc < 2 were also

considered to have support from the data (Burnham and Anderson 2002).

2.4. Results

2.4.1. Effort

Seventy-six secondary occasions (167 days of boat-based surveys) were completed

between June 2011 and May 2015 (see Appendix A2.1). In total, 410 dolphin groups

were encountered, ranging in size from one to 32 dolphins (mean = 5.7, SE 0.3;

excluding calves, see Appendix A2.6). I individually identified 346 dolphins, of

which seven were well marked but were identified from poor-quality photographs,

and were therefore excluded from further analyses. Among the 339 individuals, 134

individuals were excluded from the mark-recapture analyses because of

insufficiently marked dorsal fins (see Appendix A2.7). The overall proportion of

distinctly marked individuals was 0.78 (SE 0.02) and varied from 0.69 (SE 0.06) for

individuals captured in GR to 0.80 (SE 0.02) for individuals captured in SCR.

2.4.2. Model selection

Results from the program CloseTest indicated that the population was closed over 13

of the 16 primary periods, indicating that the assumption of population closure was

satisfied on > 81% of cases, with no significant gains or losses (Appendix A2.2).

Goodness-of fit (GOF) test results, based on multistate and subcomponent tests in U-

CARE, suggested an overall heterogeneity in capture probability (χ2 = 216.551, d.f.

= 145, P-value < 0.01, see Appendix A2.5 for summary of GOF tests). The estimate

of the variation inflation factor ĉ was < 1 (ĉ = 0.75), suggesting no substantial

overdispersion, which meant there was no need for Quasi-likelihood (QAICc)

adjustments to define the most parsimonious model (Cooch and White 2005). For

Scenario 1 (three sites), the best-fitting model, based on the AICc weight, was that

capture probability varied by site and primary period [p(site × primary period)],

apparent survival rate varied by primary period but not site [φ(primary period)], and


28

transitions varied between each site [ψ(site)] (see Appendix A2.8). For Scenario 2

(two sites), the best-fitting model was that capture probability varied by site and

primary period [p(site × primary period)], apparent survival varied by site [φ(site)]

and transitions varied between site [ψ(site)] (see Appendix A2.8). Due to having

small numbers of animals, I did not allow for capture probabilities to vary among

secondary occasions. Individual heterogeneity in capture probability was therefore

not modelled (which can be accommodated in conventional RD analyses, given

sufficient data) despite this frequently being found in photo-identification studies of

cetacean populations.

2.4.2.1. Capture probabilities

Capture probabilities varied by site and primary period (Figure 2.3). Regardless of

the scenario, the SCR had high capture probability (mean, = 0.30, min = 0.11, max

= 0.52, SE 0.03). In contrast, capture probability in GR was low (mean, = 0.06,

min = 0.00, max = 0.12, SE 0.01; Figure 2.3). Sighting frequencies showed that

individuals with higher sighting frequency were seen in SCR (>17 sightings),

whereas 50% of individuals observed in GR were seen only once (see Appendix

A2.9). Probability of captures for CS/OA were moderate (mean, = 0.15, min =

0.08, max = 0.27, SE 0.01; Figure 2.3). Coefficient of variation (CV) of the

estimated capture probability varied by site (Appendix A2.10) with GR having the

highest CV (CVmedian = 30%), thus suggesting high heterogeneity in capture

probability in comparison to CS/OA for which the CV was lower (CVmedian = 15%).


29

Figure 2.3. Capture probability (p) yielded by the models for each secondary

occasion represented as box plot (min; Quartile 1; median; Quartile 3; max) for each

Scenario: 1 – three sites (SCR = Swan Canning Riverpark, CS/OA = Cockburn

Sound/Owen Anchorage, GR = Gage Roads); and 2 – two sites (SCR and Coastal).

2.4.2.2. Apparent survival estimates and abundances

Models yielded apparent survival rates ( ) ranging from 0.93 (SE 0.03) to 1 (SE

0.00), although was higher in SCR ( = 0.98, SE 0.04) than in the pooled

Coastal site ( = 0.83, SE 0.02) in the Scenario 2.

Total estimated abundances in the SCR were low but stable over the study period

(N = 16, min 10, max 23) (Figure 2.4). Also, individuals were frequently

resighted in the SCR (see Appendix A2.9).

No obvious seasonal variation in abundance estimates was detected in the CS/OA

site (N = 103, min 71, max 147, Figure 2.4). Abundance estimates in GR

varied with the highest in winter 2011 (N = 172, 95% CI 53-561) and autumn

2015 (N = 172, 95% CI 78-381; Figure 2.4). No dolphins were ‘captured’ in

GR in summer 2012 and winter 2014.


30

Figure 2.4. Seasonal estimated abundances (Ntotal ± 95% confidence intervals) for

(a) Scenario 1 – three sites (SCR = Swan Canning Riverpark, CS/OA = Cockburn

Sound/Owen Anchorage, GR = Gage Roads) and (b) Scenario 2 – two sites (SCR

and Coastal). Lines between data points have been used for illustrative purposes

only; continuity of values is not implied. Sites are as follows: SCR (red), CS/OA

(yellow), GR (purple) and Coastal in Scenario 2 (grey).


31

2.4.2.3. Transitions

The estimates of the transition probabilities ( ) yielded by the model in Scenario 1

suggested that there was very little or no transition between the SCR and GR sites

( < 0.010) (Table 2.1). The model yielded a higher transition probability

from the SCR to CS/OA ( = 0.151, SE 0.028) than in the opposite

direction ( = 0.028, SE 0.005). Estimates from Scenario 2 also indicated

similar transition probabilities with higher transition from the SCR to Coastal sites

= 0.158, SE 0.029) and low rate in the opposite

direction 0.017, SE 0.003).

Chapter 2 - Population structure and dynamic assessed by MSCRD

32

Table 2.1. Estimates of transition probability ψ (SE) between sites for (a) Scenario 1 (three sites: SCR = Swan Canning Riverpark, CS/OA =

Cockburn Sound/Owen Anchorage, GR = Gage Roads) and (b) Scenario 2 (two sites: SCR vs. Coastal).

Transition Into:

From: SCR CS/OA GR Coastal

(a) Scenario 1

SCR 0.840 0.151 (0.028) 0.009 (0.008) -

CS/OA 0.028 (0.005) 0.921 0.051 (0.009) -

GR 0.000 (0.002)* 0.084 (0.014) 0.916 -

(b) Scenario 2

SCR 0.842 - - 0.158 (0.029)

Coastal 0.017 (0.003) - - 0.983

Note: Values in italic represent rates when staying in the same site. * Values estimated were smaller than 0.001.


33

2.5. Discussion

Three broad results emerged from the use of a MSCRD with geographic sites as

‘states’ in a complex coastal environment with estuarine, embayment, and open

coastline components and with a species known to exhibit fine-scale population

structure in such systems. First, the heterogeneity of capture probabilities between

sites showed a clear spatial component, consistent with some degree of population

structuring. Second, estimates of abundance and apparent survival rate allowed some

inference about the status of each site-related grouping (or ‘subpopulation’, in the

metapopulation model). Finally, estimates of transition probability between sites

indicated some degree of connectivity between those site-related groupings.

2.5.1. Spatial heterogeneity

Differences in capture probability appear to reflect individual variation in the use of

(and fidelity to) a site. Heterogeneity in capture probability has also previously been

linked to variation in individual or group ranging patterns (Crespin et al. 2008; Urian

et al. 2014). Here, the capture probability was high in the estuary (SCR) and low in

the open coastline (GR), suggesting that the ranging patterns of dolphins using those

sites differ markedly in, e.g., home range size, site fidelity, seasonal or year-round

occupancy and habitat use (Sprogis et al. 2015). The capture probabilities for the

estuary are consistent with the long-term site fidelity and year-round occupancy

reported in Chabanne et al. (2012). In contrast, Waples (1997) suggested that

dolphins in the open coastline north of Perth likely range over many kilometres of

coastline and are only intermittently present in particular areas, again consistent with

the low capture probabilities observed here.

In addition, coefficients of variation for GR were high, suggesting more individual

heterogeneity in capture probability due to factors such as large and variable home

range sizes or avoidance or attraction responses to boats (Pollock et al. 1990). I

acknowledge that the estimates of demographic parameters for GR could be biased

with lower estimates of apparent survival leading to underestimated abundances

(Pollock et al. 1990; Williams et al. 2002). This outcome indicates the practical

difficulties for MSCRD approaches if the ranging patterns (or other characteristics)


34

of the individuals present at a site are such that CVs will be high, even where

sampling is relatively intensive and is sustained over multiple years.

In contrast, the low coefficient of variation in the capture probabilities for CS/OA

(CV = 15%) suggested that majority of the individuals were equally captured.

Furthermore, despite lower capture probabilities than those estimated in SCR,

dolphins nonetheless occurred year-round in CS/OA. Differences in capture

probabilities between CS/OA and SCR may reflect larger home ranges for

individuals in the embayment system (Sprogis et al. 2016). Site configuration may

also influence individual detection with a greater likelihood of detecting individuals

in narrow areas such as channels or rivers than in wide, unconfined areas such as

open water that have with no prominent barriers.

2.5.2. Apparent survival and abundance estimates

Given that the majority of the individuals were not sexed, I acknowledge that

estimates of the apparent survival rates obtained through the best-fitting models may

be overestimated for sexed individuals and underestimated for unsexed individuals.

Also, most of the sexed individuals were those regularly seen during the study period

because collection of genetic samples (for which sex determination was one

objective) was preferentially undertaken on well-known individuals. While Nichols

et al. (2004) demonstrated how to deal with unsexed individuals in capture-recapture

analytical approaches, that method could not be applied in this study due to the

complexity of the models. Apparent survival of dolphins in SCR was high (0.98),

illustrating an almost complete lack of permanent emigration during the study. In

addition, consistent abundance estimates across the course of the study (c. 16

dolphins), along with high individual resighting rates, indicated the long-term

residency of the SCR subpopulation.

In contrast, an apparent survival estimate of 0.83 in the pooled Coastal site is

indicative of permanent emigration of individuals, suggesting both resident and more

transient components (Brown et al. 2014; Palmer et al. 2015). Variation in

abundance estimates in conjunction with apparent survival rates can assist in making

inferences about residency status (Brown et al. 2016). The high variability in


35

abundance estimates in GR coupled with the large number of individuals sighted

only once (and not seen in SCR or CS/OA) is consistent with transient occupancy

patterns for dolphins at that site. This agrees with other studies indicating that

bottlenose dolphins are often more abundant in open coast environments where

individuals tend to have larger home range size, which may reflect both food

availability and foraging tactics (Sprogis et al. 2015; McCluskey et al. 2016).

2.5.3. Transitions

There was a substantial difference in transition probabilities between the SCR and

the coastal sites. The reasons for this are not clear. As the mouth of the SCR estuary

is located at the junction between the CS/OA and GR sites, travel distance between

sites should not be a factor. Furthermore, transitions from SCR were also limited to

CS/OA. One possibility is that the environment of OA (shallow, protected waters

with extensive seagrass meadows) may be more suitable habitat for SCR individuals

than the GR environment.

Conversely, transitions from CS/OA to SCR were limited, although occasional

visitors were documented in the lower reaches of the estuary or further up river,

sometimes escorted by SCR males (Connor et al. 1996, 2000a). Those transitions in

and out from SCR were consistent with the emigration and reimmigration

demographic model reported in Chabanne et al. (2012). The long-term connection

between SCR and CS/OA suggests a certain degree of gene flow between the

subpopulations.

2.5.4. Limitations

The transitions of animals in and out of the study area (i.e., sometimes referred to as

the “edge effect”, Otis et al. 1978) present two significant problems for MSCRD

studies. The first is that these transitions increase the heterogeneity of captures in the

study area at large (Crespin et al. 2008; Brown et al. 2016). The second is that sites

within the study area may differ in the degree to which such edge effects occur. In

this study, for example, I found more heterogeneity associated with edge effects in

the open coastline (GR) than in the estuary (SCR). When considering predefined


36

sites, it is advisable to consider what proportion of the individuals captured in that

site may also be captured in other sites and whether individual heterogeneity in

capture probability may differ between sites. This is particularly relevant if sites

differ greatly in size or in other features that may limit detectability and the precision

of estimation (Burgess et al. 2014; Palmer et al. 2015). Here, I benefitted from

existing information on the likely ranging patterns of individuals, but was

nonetheless unable to implement a study design that negated the heterogeneity of

captures arising from the transitions of individuals into and out of the study area.

The assumption that no transition between sites occurs within a primary period

(Arnason 1972, 1973) is difficult to validate, particularly when sites are juxtaposed

(i.e., no physical barrier and distance exists). Such violations may result in greater

heterogeneity in capture probability between individuals and within sites (i.e.,

individuals captured within a site do not all have the same survival rate). Here, 2.6%

of the captures violated this assumption. The extent to which this assumption can be

acceptably violated is unclear, although it has been reported that < 1% of violated

occasions would create a small bias (O'Connell-Goode et al. 2014). This issue was

dealt with in this study by pooling the coastal sites (Scenario 2) (Schwarz 2002),

while also ensuring that all sites were sampled equally (Crespin et al. 2008).

However, this procedure may lead to more heterogeneity in capture probabilities and

bias survival rates and abundance estimates (Pollock et al. 1990). Here, the survival

rate for dolphins in CS/OA was higher than for GR, which was also supported by a

consistency in abundance estimates and moderate resighting rates in CS/OA with

few individuals being seen only once.

Low capture probabilities make it difficult to obtain reliable estimates of apparent

survival rate and abundance (Pollock et al. 1990; Rosenberg et al. 1995). It is

advisable that capture probabilities of at least 0.10 per secondary occasions be

obtained for reasonable results (Lettink and Armstrong 2003). Although common in

studies of wide-ranging and low density species (Harmsen et al. 2010; Palmer et al.

2015), there are few obvious measures for dealing with low capture probabilities

other than increasing sampling effort (Pollock et al. 1990; Rosenberg et al. 1995).

However, increases in sampling effort involve additional cost (Tyne et al. 2016) and


37

time outlays that must be multiplied by the number of sites (to ensure that each site

is surveyed equally).

Finally, I note that the sophistication and utility of MSCRD models continue to

evolve, notably in relation to the modelling of TE, which can alleviate some of the

large differences in survival estimates (Bailey, Converse & Kendall 2010). I was

unable to model TE in this study as the models would not estimate the applicable

parameters, due to the small population sizes. Rankin et al. (2016) also discussed

issues linked to low capture probabilities and the estimation of TE and suggested use

of hierarchical Bayesian models for the Robust design.

2.5.5. Conclusion

This study, which explored the implications of heterogeneity in capture probabilities

for MR studies within a MSCRD framework, demonstrated a valuable approach for

assessing the dynamic, status and connectivity of multiple subpopulations of a

behaviourally plastic species within a heterogeneous environment. I also showed that

a MSCRD study design can assess transitions between predefined geographic sites,

and thus assist in understanding dynamic processes between subpopulations within a

metapopulation. A MSCRD incorporating geographic sites associated with

anthropogenic impacts or climate change may be a powerful tool for management

and conservation of species that are amenable to a MR study. The short-term

transition of individuals between putative subpopulations is particularly relevant for

the conservation of highly mobile species (e.g., birds, larger mammals) in

environments where anthropogenic pressures vary greatly from one geographic ‘site’

to another.


38

Appendices

Appendix A2.1. Survey effort

Table A2.1.1. Summary of survey effort and time interval between efforts across

primary periods (i.e., seasons) for four years (June 2011-May 2015). A secondary

occasion refers to a combination of the four surveys covering the entire study area.

“in secondary occasion” refers to the number of days required to complete a

secondary occasion, while “out secondary occasion” refers to the number of days

between the last survey of the secondary occasion i and the first survey of the

secondary occasion i + 1.

Effort Interval time

Year Seasons Time

(hrs)

Distance

(km)

#

secondary

occasion

# days in

secondary

occasion

(SE)

# days out

secondary

occasion

(SE)

# days

between

seasons

2011 Winter 53.1 NA 6 2.17 0.17 11.20 9.01

Spring 42.6 663 5 3.20 1.20 14.50 2.1 12

2012

Summer 43.9 672 5 2.20 0.20 13.25 3.33 27

Autumn 40.8 662 5 2.00 0.00 17.50 14.24 16

Winter 40.5 670 5 2.20 0.20 4.25 2.66 32

Spring 43.2 669 5 3.00 0.45 7.25 1.55 80

2013

Summer 43.9 675 5 3.00 0.55 3.75 1.75 58

Autumn 39.2 663 5 2.00 0.00 4.25 1.31 45

Winter 42.1 678 5 2.00 0.00 9.50 4.77 59

Spring 31.3 534 4 2.00 0.00 7.33 2.19 41

2014

Summer 32.8 531 4 3.75 1.03 7.67 4.26 77

Autumn 36.0 547 4 2.00 0.00 8.67 4.18 38

Winter 45.3 676 5 2.00 0.00 9.00 2.45 63

Spring 26.3 417 3 4.67 0.88 8.50 5.5 57

2015 Summer 44.5 683 5 2.40 0.24 6.75 2.43 52

Autumn 45.3 686 5 3.00 0.77 3.50 0.65 53

TOTAL 650.8 c. 10,000.00 76 2.60 0.20 8.63 1.00 47.33 5.16


39

Appendix A2.2. The CloseTest: a method to investigate of the population

closure

I investigated the assumption of population closure for each primary period

separately with all sites converted to a single site using the Stanley and Burnham

(1999) and Otis et al. (1978) closure tests, as implemented in the computer program

CloseTest (Stanley and Burnham 1999). The two tests were developed on different

null hypotheses and, if used in conjunction, allow for better detection and

interpretation of closure violations in capture-recapture datasets. The Stanley and

Burnham (1999) closure test allows for time variation in capture probabilities in the

absence of behavioural responses and heterogeneity. The Otis et al. (1978) closure

test allows for investigation of population closure in the presence of heterogeneity in

capture probabilities.

Table A2.2.1. Closure tests for each primary period using Stanley and Burnham

(1999) and Otis et al. (1978) implemented in the program CloseTest. Null hypothesis

(no closure) was rejected when P-value < 0.05 at both tests.

Primary

period

# secondary

occasions

Otis et al. test

Stanley and Burnham

test

z-value P-value

χ2 P-value

1 6

1.31 0.9

19.3 0.01

2 5

-3.3 0

10.37 0.11

3 5

insufficient data

4.24 0.12

4 5

-1.46 0.01

15.6 0.01

5 5

-0.7 0.24

13.33 0.02

6 5

1.02 0.85

9.63 0.14

7 5

1.45 0.92

38.59 0.00

8 5

-2.26 0.01

24.83 0.00

9 5

-4.01 0

15.27 0.00

10 4

insufficient data

1.02 0.60

11 4

1.4 0.92

9.68 0.02

12 4

1.01 0.84

6.02 0.20

13 5

-0.71 0.24

10.26 0.07

14 3

3.77 0.99

6.35 0.04

15 5

-1.85 0.03

10.08 0.12

16 5

1.29 0.9

40.12 0


40

Appendix A2.3. Data processing for photograph quality and individual

distinctiveness

Photograph quality was measured using five parameters: clarity-focus, contrast,

angle, fin wave effects and the proportion of the frame occupied by the dorsal fin

(Urian et al. 1999). Three categories were then defined from the sum of the grades

attributed to each parameter for each photo: Q1 – good quality (6-9), Q2 – average

quality (10-12) and Q3 – poor quality (> 12). Only Q1 and Q2 photographs were

used to identify dolphins and in the analyses.

Individual dolphins were primarily identified based on the nicks and scars on the

leading and trailing edges of the dorsal fin (Würsig and Jefferson 1990). Temporary

markings such as rake marks and skin lesions only visible for a short-term were also

used for individuals regularly sighted. Each dolphin was assigned a grade for

distinctiveness of their dorsal fin: D1 - very distinctive fin (i.e., unique features

evident even in distant or poor quality photograph); D2 - moderately distinctive fin

(e.g., distinctive markings involve subtle nicks); and D3 non-distinctive fin (e.g., fin

lacks distinctive markings, a “clean fin”). Mark-recapture analysis was performed

using photos of quality Q1 and Q2 for D1 and D2 individuals only.


41

Appendix A2.4. Method to estimate proportion of distinctly marked individuals

and correction of the marked abundance estimates for consideration of the

proportion of unmarked individuals (D3 individuals identified from Q1 and Q2)

I calculated mark rates for each site (θ) and adjusted the estimates of population size

by following the method described in Nicholson et al. (2012), Tyne et al. (2014),

Brown et al. (2016) and Sprogis et al. (2016).

Only sightings where all photographed individuals were identified from a good

photo quality (Q1 and Q2) image and without regard to the distinctiveness of their

dorsal fins were used to estimate the proportion of marked individuals in the

population (θ). Marked rates were calculated for the full study period per site.

Population size estimates were corrected to consider the proportion of unmarked

individuals (D3 individuals identified from Q1 and Q2 photos, see Appendix A2.3).

The total number of dolphins (per site) with distinctive fins (D1 and D2) was divided

by the total number of dolphins (per site) encountered in the selected sightings, as

follows:

N N ⁄ ,

where N is the estimated total population size, N the estimated of the distinctly

marked population size, and the estimate proportion of distinctly marked

individuals in the population. All parameters are estimated for each site. The

approximate variance for the estimated total population size for overall or per site

was derived using the following formula for the standard error of a ratio (Williams et

al. 2002):

(N ) √ N ( ( )

) ,

where n is the number of marked individuals captured within each site.

Log-normal 95% confidence intervals were calculated, with a lower limit of

N N and upper limit of N

N ,

where:


42

exp ( √ln( (

)

)) ,

(Burnham et al. 1987).


43

Appendix A2.5. Test for heterogeneity in capture probabilities by implementing

goodness-of-fit tests for multistate models using the program U-CARE

Pollock’s closed robust design models do not have a goodness-of-fit (GOF) test to

validate the assumptions of equal probabilities of capture and survival between

individuals. However, I tested the fit of the data using the program U-CARE

(Choquet et al. 2009). Secondary occasions within each primary period were pooled,

although the test allows for multiple sites (Pradel et al. 2005).

In U-care, the goodness-of-fit test was divided into three categories: WBWA test for

a memory effect; two 3G component tests (3G.Sr and 3G.Sm) for evidence of

transience; and two M component tests (ITEC and LTEC) for trap-dependence. If

adjustment of the starting model structure was required (based on tests), a variance

inflation factor (ĉ) was then estimated by dividing the Pearson statistic of the sum of

each test component (χ2) by its degrees of freedom (d.f.), excluding components that

were structurally adjusted (Choquet et al. 2009).

Table A2.5.1. Summary of U-CARE test results for bottlenose dolphin study during

2011-2015. Global goodness-of-fit tests were divided into three categories: WBWA

test for a memory effect; two test 3G components test for evidence of transience; and

3G.SR for transience; M.ITEC for trap dependence; and. Tests statistics (χ2),

corresponding degrees of freedom (d.f.) and P-values are given. See Choquet et al.

(2009) and Pradel et al. (2005) for more details on component tests.

Tests χ2 d.f. P-value

WBWA 65.735 26 0.000

3G.Sr 29.324 21 0.106

3G.Sm 34.073 63 0.999

M.ITEC 63.584 21 0.000

M.LTEC 23.835 14 0.048

Note: Significant P-values are in bold.


44

Appendix A2.6. Bottlenose dolphin groups

Table A2.6.1. Number of bottlenose dolphin groups per site (SCR = Swan Canning

Riverpark, CS/OA = Cockburn Sound/Owen Anchorage, GR = Gage Roads) and per

season of each year (16 seasons, 4 years, aka primary periods).

Season # Group

Year SCR CS/OA GR

2011 Winter 6 13 5

Spring 11 12 5

2012

Summer 5 11 0

Autumn 11 10 5

Winter 13 15 3

Spring 9 18 2

2013

Summer 15 13 1

Autumn 8 17 12

Winter 10 13 6

Spring 3 14 3

2014

Summer 3 16 3

Autumn 7 13 3

Winter 12 15 0

Spring 6 8 2

2015 Summer 9 15 5

Autumn 10 13 1

Sub-Total

138 216 56

TOTAL

410


45

Table A2.6.2. Group size (Mean, SE, Min, and Max) per site (SCR = Swan Canning Riverpark, CS/OA = Cockburn Sound/Owen Anchorage,

GR = Gage Roads) and per season of each year (16 seasons, 4 years, aka primary periods).

Season SCR CS/OA GR

Year Mean SE Min Max Mean SE Min Max Mean SE Min Max

2011 Winter 3.2 0.6 1.0 8.0

7.5 1.2 1.0 21.0

8.6 2.2 1.0 13.0

Spring 4.2 0.7 1.0 8.0 7.2 1.4 1.0 25.0 6.4 2.6 1.0 14.0

2012

Summer 3.2 0.7 1.0 4.0 5.2 1.4 2.0 11.0 - - - -

Autumn 3.7 0.5 1.0 6.0

6.8 1.5 1.0 15.0

6.2 3.0 3.0 18.0

Winter 3.0 0.6 1.0 8.0

3.5 1.0 1.0 20.0

7.0 5.0 2.0 17.0

Spring 3.4 1.7 1.0 9.0 7.1 1.3 1.0 20.0 8.0 6.0 2.0 14.0

2013

Summer 3.3 0.9 1.0 9.0 4.6 1.7 1.0 17.0 17.0 - 17.0 17.0

Autumn 5.8 0.6 3.0 8.0

4.3 0.9 1.0 15.0

6.0 1.1 2.0 13.0

Winter 2.9 0.2 1.0 5.0

6.6 0.4 2.0 14.0

5.0 1.4 1.0 10.0

Spring 4.0 0.9 2.0 7.0 4.6 3.0 1.0 16.0 8.0 3.2 2.0 14.0

2014

Summer 3.7 1.7 2.0 5.0 7.4 0.9 1.0 23.0 5.7 3.2 2.0 12.0

Autumn 3.1 1.1 1.0 8.0

8.4 1.7 2.0 24.0

4.7 2.2 2.0 9.0

Winter 5.5 1.0 1.0 13.0

3.9 0.7 1.0 10.0

- - - -

Spring 4.2 0.7 1.0 8.0 9.9 1.8 3.0 27.0 5.0 4.0 1.0 9.0

2015 Summer 5.0 0.7 1.0 14.0

6.1 2.5 1.0 14.0

13.0 4.2 2.0 22.0

Autumn 4.6 1.0 1.0 11.0 10.4 2.1 1.0 23.0 32.0 - 32.0 32.0

Overall 3.9 0.2 1.0 14.0

6.3 0.4 1.0 27.0

7.6 0.9 1.0 32.0


46

Appendix A2.7. Bottlenose dolphin individuals

Table A2.7.1. Summary of the number of identified individuals of bottlenose

dolphins (and marked only) by site (SCR = Swan Canning Riverpark, CS/OA =

Cockburn Sound/Owen Anchorage, GR = Gage Roads) in each season of each year

(16 seasons, 4 years) and from good photo quality only.

Year Season Site

SCR CS/OA GR

Year 1

(2011-2)

Winter 12 (10) 70 (52) 35 (27)

Spring 17 (13)

76 (57)

28 (18)

Summer 9 (6)

39 (30)

0 (0)

Autumn 20 (16) 47 (39) 29 (23)

Year 2

(2012-3)

Winter 19 (14)

46 (41)

15 (15)

Spring 16 (10)

81 (61)

14 (14)

Summer 18 (14)

46 (36)

13 (9)

Autumn 16 (10)

59 (39)

49 (33)

Year 3

(2013-4)

Winter 20 (12) 64 (41) 24 (17)

Spring 8 (5)

52 (36)

21 (20)

Summer 8 (6)

78 (51)

14 (9)

Autumn 16 (10) 66 (43) 14 (7)

Year 4

(2014-5)

Winter 22 (15)

42 (21)

0 (0)

Spring 20 (11)

59 (39)

10 (7)

Summer 24 (14)

63 (42)

54 (32)

Autumn 22 (14)

90 (60)

32 (29)

Overall 37 (25) 218 (139)

170 (107)

Total (all sites confounded) 343 (209)


47

Appendix A2.8. Multistate closed robust design models summary

Table A2.8.1. Multistate closed robust design models (in rank order of AICc scores) for each scenario: Scenario 1 with three sites and Scenario 2

with two sites. The table provides an overview of the Akaike Information Criterion corrected for small sample size (AICc), difference in AICc

with best-fitting model and AICc weight, the number of parameters used in model fit and the deviance explained.

Models AICc ΔAICc AICc

weight

Model

likelihood Parameters Deviance

Scenario 1 (three states)

φ(period) ψ(site) p(site × period) N(site × period), p=c 3574.0 0.0 0.994 1.0000 114 3326.1

φ(.) ψ(limited-site) p(site × period) N(site × period), p=c 3585.0 11.0 0.004 0.0041 95 3381.3

φ(site) ψ(limited-site) p(site × period) N(site × period), p=c 3587.0 13.0 0.002 0.0015 96 3381.0

φ(.) ψ(.) p(site × period) N(site × period), p=c 3633.0 59.0 0.000 0.0000 95 3429.3

φ(.) ψ(limited-site × period) p(site × period) N(site × period), p=c 3645.8 71.7 0.000 0.0000 138 3340.1

φ (period) ψ(.) p(site × period) N(site × period), p=c 3655.8 81.8 0.000 0.0000 108 3422.0

Scenario 2 (two states)

φ(site) ψ(site) p(site × period) N(site × period), p=c 2292.5 0.0 0.923 1.0000 65 2156.3

φ(.) ψ(site) p(site × period) N(site × period), p=c 2297.5 5.0 0.077 0.0834 64 2163.4

φ(site) ψ(site × period) p(site × period) N(site × period), p=c 2309.5 17.0 0.000 0.0002 95 2106.0

φ(period) ψ(site) p(site × period) N(site × period), p=c 2311.4 18.9 0.000 0.0001 79 2144.1

φ(.) ψ(site × period) p(site × period) N(site × period), p=c 2313.1 20.6 0.000 0.0000 93 2114.2

φ(period) ψ(site × period) p(site × period) N(site × period), p=c 2326.5 34.0 0.000 0.0000 107 2095.2

Note: φ apparent survival; ψ transition rate; p probability of capture; p = c probability of capture is equal to recapture; N abundance; (.) constant; (site)

varying by site; (limited-site) varying by site but restricted to symmetric movement such that ψ(1 2) = ψ(2 1); (period) varying by primary period; (site

× period) varying by site and primary period; (limited-site × period) varying by site (restricted to symmetric movement) and primary period.


48

Appendix A2.9. Sighting frequency

Figure A2.9.1 Sighting frequency of adult/sub-adult dolphins observed in the metropolitan waters of Perth from June 2011 to May 2015: Top –

for all; Bottom – per site for individuals seen in more than 10% of the surveys. GR = Gage Roads, SCR = Swan Canning Riverpark, CS/OA =

Cockburn Sound/Owen Anchorage.


49

Appendix A2.10. Coefficient of variations (CV) of capture probability (p)

Figure A2.10.1. Coefficient of variations (CV) of each capture probability (p)

represented as box plot (min; Quartile 1; median; Quartile 3; max) for each Scenario:

Scenario 1 – three sites (SCR = Swan Canning Riverpark, CS/OA = Cockburn

Sound/Owen Anchorage, GR = Gage Roads); and Scenario 2 – two sites (SCR and

Coastal).


50

Appendix A2.11. MSCRD analysis using sex as an individual covariate

Materials and Methods

In MSCRD models, I also attempted to address individual heterogeneity in capture

probabilities by including sex as individual covariate (i.e., female, male or

unknown). Individuals were sexed in-situ based on the direct observation of the

genitals or on the presence of a dependent calf. Additionally, biopsy samples were

collected throughout the entire study area between 2007 and 2015 using a remote

biopsy system designed for small cetaceans by PAXARMS (Krützen et al. 2002). I

then molecularly sexed individuals following the method described in Brown et al.

(2014).

In MARK, each MSCRD model combination was run with the probability of capture

(p) varying by sex, site and/or primary period or constant, and with recapture

probability (c) set as equal to first capture probability (p). The abundance (N) was set

to vary by sex, site and primary periods [N(sex × site × primary periods)]. Several

sub-models for apparent survival (φ) were run (i.e., whether it varied by sex and/or

site or none). Probability of transition between sites (ψ) was also estimated whether

it varied by sex and/or site or none. Time intervals between primary periods were

specified as a fraction of a year (i.e., seasons). Thus, when apparent survival and

transitions probabilities were time-constant estimated, their estimates were annual.

Goodness-of-fit tests were re-run for each sex class: females, males and not sexed

individuals following the same protocol as previously (see Appendix A2.5). Models

were ranked using the Akaike Information Criterion (AICc, Burnham and Anderson

2002). The model with most support by AICc (highest AICc weight) was selected as

the most parsimonious model. Models with ΔAICc < 2 were also considered to have

support from the data (Burnham and Anderson 2002).


51

Results

Among the 339 individuals, 108 females and 61 males were identified although 134

individuals (including 40 females and 11 males) were not included in the mark-

recapture analysis because of insufficiently marked dorsal fins.

Model selection and Capture probabilities

Goodness-of fit based on multistate and subcomponent tests in U-CARE indicated

no heterogeneity in capture for any of the sex classes (χ2

females = 75.093, d.f. = 67, P-

value = 0.233, χ2

males = 46.780, d.f. = 60, P-value = 0.894, and χ2

not sexed = 30.719, d.f.

= 56, P-value = 0.998, see Table A2.11.1 for summary of GOF tests). I did not adjust

the Quasi-likelihood (QAICc) for substantial overdispersion, which meant the most

parsimonious model was defined under no adjustment (Cooch and White 2005).

The best fitting model, based on the AICc weight, for Scenario 1 (three sites) was

that capture probability varied by site and primary period [p(site × primary period)],

apparent survival rate varied by sex [φ(sex)] and transitions between sites varied

between each site but not sex [ψ(site)] (see Table A2.11.2). For Scenario 2 (two

sites), the best-fitting model was that capture probability varied by site and primary

period [p(site × primary period)], apparent survival varied by sex class [φ(sex)] and

transitions varied between site and sex [ψ(sex × site)] (see Table A2.11.2). Due to

model constraints it was too difficult to apply the sex covariate as variable for

capture probabilities. Estimates of capture probabilities and coefficients of variation

(CV) of the capture probabilities (Figures not showed) were similar to those obtained

in MSCRD models without using sex as individual covariate (i.e., the CV was high

for GR and low for CS/OA with CV = 31% and 15%, respectively).


52

Table A2.11.1. Summary of U-CARE test results for bottlenose dolphin study during 2011-2015 per sex classes (female, male, not sexed).

Global goodness-of-fit (JMV Model) tests were divided into three categories of components: WBWA test for memory; 3G.Sr and 3G.Sm for

transience; and M.ITEC and M.LTEC for trap dependence. Tests statistics (χ2), corresponding degrees of freedom (d.f.) and P-values are given.

See Choquet et al. (2009) and Pradel et al. (2005) for more details on component tests.

Females Males Not sexed

Tests χ2 d.f. P-value χ2 d.f. P-value χ2 d.f. P-value

WBWA 29.798 14 0.008 16.347 14 0.293 6.455 6 0.374

3G.Sr 8.841 8 0.356 5.953 5 0.311 7.849 14 0.897

3G.Sm 18.229 27 0.894 8.575 29 1 9.483 32 1

M.ITEC 14.038 11 0.231 9.721 8 0.285 3.450 2 0.178

M.LTEC 4.118 7 0.766 6.184 4 0.186 3.482 2 0.175

Goodness-of-fit 75.093 67 0.233 46.780 60 0.894 30.719 56 0.998

Note: Significant P-values are in bold


53

Table A2.11.2. Multistate closed robust design models (in rank order of AICc scores) for each scenario: Scenario 1 with three sites and Scenario

2 with two sites. The table provides an overview of the Akaike Information Criterion corrected for small sample size (AICc), difference in AICc

with best-fitting model and AICc weight, the number of parameters used in model fit and the deviance explained.


weight

Model


Scenario 1 (three states)

φ(sex) ψ(site) p(site × period) N(sex × site × period), p=c 5638.0 0.0 0.983 1.0000 164 5267.3

φ(sex × site) ψ(site) p(site × period) N(sex × site × period), p=c 5646.1 8.1 0.017 0.0176 166 5270.3

φ(.) ψ(site) p(site × period) N(sex × site × period), p=c 5656.2 18.2 0.000 0.0001 161 5293.1

φ(site) ψ(site) p(site × period) N(sex × site × period), p=c 5658.3 20.3 0.000 0.0000 164 5287.6

φ(sex) ψ(sex) p(site × period) N(sex × site × period), p=c 5722.4 84.4 0.000 0.0000 159 5364.4

φ(sex × site) ψ(sex) p(site × period) N(sex × site × period), p=c 5732.4 94.4 0.000 0.0000 166 5356.6

Scenario 2 (two states)

φ (sex) ψ(sex × site) p(site × period) N(sex × site × period), p=c 4440.4 0.0 0.928 1.0000 105 4213.8

φ (sex × site) ψ(sex × site) p(site × period) N(sex × site × period), p=c 4445.5 5.1 0.072 0.0780 108 4211.9

φ (site) ψ(sex × site) p(site × period) N(sex × site × period), p=c 4459.6 19.2 0.000 0.0001 103 4237.7

φ (sex) ψ(site) p(site × period) N(sex × site × period), p=c 4460.4 20.1 0.000 0.0000 105 4233.9

φ (sex × site) ψ(site) p(site × period) N(sex × site × period), p=c 4465.7 25.3 0.000 0.0000 108 4234.1

φ (.) ψ(sex × site) p(site × period) N(sex × site × period), p=c 4466.8 26.4 0.000 0.0000 103 4244.8

Note: φ apparent survival; ψ transition rate; p probability of capture; p=c probability of capture is equal to recapture; N abundance; (.) constant;

(sex) varying by sex; (site) varying by site; (sex × site) varying by sex and site; (sex × site × period) varying by sex, site, and primary period;

(site × period) varying by site and primary period.


54

Apparent survival estimates and abundances

Given that the majority of the individuals were not sexed, I acknowledge that

estimates of the apparent survival rates obtained through the best-fitted models may

be overestimated for sexed individuals and underestimated for not sexed individuals.

In fact, most of the sexed individuals were those regularly seen during the study

period. While Nichols et al. (2004) demonstrated how to deal with not sexed

individuals in capture-recapture approaches, this method could not be applied in this

study due to the complexity of the models. Regardless of the scenarios, models

yielded the same range of apparent survival rates ( ) for females ( = 0.91-0.94,

SE 0.02) and males ( = 0.93-0.95, SE 0.02-0.04) and were lower for unknown sex

( = 0.70-0.74, SE 0.04), although such results were expected as most sexed

individuals were resighted multiple times. Biopsy sampling for dolphins is expensive

(cost of equipment and time outlays) and, thus, sampling may not be random but

target individuals that can be recognized (i.e., that have a distinctive fin) and which

are seen multiple times so that individual data (i.e., sex, capture history, grouping)

can be used for multiple purposes, including methods that require a certain quantity

of data.

No obvious seasonal variation in abundance estimates was detected in the CS/OA

site for males and females (N = 44, min 32, max 58; N = 33,

min 16, max 48), although estimates for not sexed individuals were more variable

N = 23, min 16, max 56) (Figure A2.11.1). Abundance estimates for

females in GR varied with the highest in summer 2013 (N = 91, 95% CI 24-

346, Figure A2.11.1) while there was no record in summer 2012 and winter 2014

(N = 0). Only two not sexed individuals were recorded in the SCR during

autumn and winter 2012. In other sites, however, estimated abundances of not sexed

individuals varied with GR showing more variable and less precise estimates

(N = 53 individuals, min 0, max 127, SE 0-88) (Figure A2.11.1).


55

Figure A2.11.1. Seasonal estimated abundances (Ntotal ± 95% confidence intervals)

by sex (column) and by Scenario (row): 1 – three sites (SCR = Swan Canning

Riverpark, CS/OA = Cockburn Sound/Owen Anchorage, GR = Gage Roads) and 2-

two sites (SCR and Coastal). Lines between data points have been used for

illustrative purposes only; continuity of values is not implied. Sites are: SCR (red),

CS/OA (yellow), GR (purple) and Coastal in Scenario 2 (grey). Sexes are: females

(circle), males (square) and unknown (triangle).

Movements

The estimates of the transition probabilities ( ) yielded by the model in Scenario 1

suggested that there was very little or no movement between the SCR and GR sites

( < 10-3

) (Table A2.11.2). Model yielded higher movement rate from the

SCR to CS/OA ( = 0.153, SE 0.028) than in the opposite direction

( = 0.026, SE 0.005). Estimates from Scenario 2 also indicated similar

transition rates for females ( = 0.128, SE 0.040) and males ( = 0.157, SE

0.005) from the SCR to coastal waters, although rates in the opposite direction were

lower .


56

Table A2.11.2 Estimates of movement transitions ψ (SE) between sites for (a)

Scenario 1 (three sites: SCR = Swan Canning Riverpark, CS/OA = Cockburn

Sound/Owen Anchorage, GR = Gage Roads) and (b) Scenario 2 (two sites: SCR vs.

Coastal). In comparison to Scenario 1, estimates for Scenario 2 varied by sex. Values

in italic represent rates when staying in the same site.

(a)

Note: * Values estimated were smaller than 0.001.

(b)

Note: ** ψUnknown moving from SCR to Coastal waters was estimated to 1 when

using the last capture approach.

Movement Into:

From: SCR CS/OA GR

All

SCR 0.847 0.153 (0.028) 0.000 (0.000)*

CS/OA 0.026 (0.005) 0.917 0.057 (0.010)

GR 0.000 (0.002)* 0.099 (0.015) 0.901

Movement Into:

From: SCR Coastal

Females

SCR 0.872 0.128 (0.040)

Coastal 0.016 (0.005) 0.984

Males

SCR 0.843 0.157 (0.038)

Coastal 0.035 (0.005) 0.965

Unknown

SCR 0.351 0.649 (0.277)**

Coastal 0.002 (0.002) 0.998

57

Chapter 3. Identifying the relevant local population for

environmental impact assessments of mobile marine fauna

3.1. Abstract

Environmental impact assessments must be addressed at a scale that reflects the

biological organisation for the species affected. It can be challenging to identify the

relevant local wildlife population for impact assessment for those species that are

continuously distributed and highly mobile. Here, I document the existence of local

communities of Indo-Pacific bottlenose dolphins (Tursiops aduncus) inhabiting

coastal and estuarine waters of Perth, Western Australia, where major coastal

developments have been undertaken or are proposed. Using sighting histories from a

four-year photo-identification study, I investigated fine-scale, social community

structure of dolphins based on measures of social affinity, and network (half-weight

index - HWI, preferred dyadic association tests, and lagged association rates - LAR),

home ranges, residency patterns (lagged identification rates - LIR), and genetic

relatedness. Analyses revealed four socially and spatially distinct, mixed-sex

communities. The four communities had distinctive social patterns varying in

strength, site fidelity, and residency patterns. Overlap in home ranges and relatedness

explained little to none of the association patterns between individuals, suggesting

complex local social structures. The study demonstrated that environmental impact

assessments for mobile, continuously distributed species must evaluate impacts in

light of local population structure, especially where proposed developments may

affect core habitats of resident communities or subpopulations. Here, the risk of local

extinction is particularly significant for an estuarine community because of its small

size, limited connectivity with adjacent communities, and use of areas subject to

intensive human use. In the absence of information about fine-scale population

structure, impact assessments may fail to consider the appropriate biological context.

Chapter 3 - Identifying local population for EIA

58

3.2. Introduction

Applied wildlife research can improve the scientific basis for environmental impact

assessment (EIA) by developing methodologies to evaluate impacts of human

activities on wildlife (Morrison et al. 2006; Steidl and Powell 2006; Bejder et al.

2009, 2012; Torres et al. 2016). However, for such evaluations to be effective, they

must also be directed at an appropriate scale of biological organisation for the

species to be impacted by a proposed development or activity. Here, I describe a

methodology to identify the relevant local population for EIA which is suitable for

species that are continuously distributed and highly mobile.

While the procedural and formal requirements for EIA are often closely prescribed

by statutes, regulations, and associated policy and guidance documents, there are

often few specific requirements as to the scientific information necessary for EIA.

While many jurisdictions have now developed policies or protocols for biodiversity

surveys to allow for the identification of fauna and flora species that may affected by

a proposed development or activity, prescriptive guidelines for the conduct of field

studies of human impacts on wildlife, as set by the administrative bodies having

statutory responsibility for EIA, remain uncommon. As such, it is vital for wildlife

researchers to identify best-practice methodologies for field-based impact assessment

research, so as to encourage their use in studies undertaken to support EIAs.

Broadly speaking, the aim of EIA is to conduct a detailed assessment of the potential

impacts of a proposed development or activity on a particular environment

(including the biota occurring there) on which decision-makers can then rely in

determining whether the proposed development or activity should be approved and,

if so, with what conditions (Glasson et al. 2012). Ideally, the EIA process for a

proposed development or activity should consider the range of possible impacts on

wildlife in a manner that is species-, site-, and (if applicable) season-specific (Fox et

al. 2006). If the assessment methods employed are inappropriate or are inadequately

implemented, the EIA outcomes (typically an environmental impact statement or

report) may be incomplete and inaccurate. An obvious example is an EIA based on

sparse and opportunistic sighting data for a species (Bejder et al. 2012).


59

To adequately characterise the impact of a proposed development or activity on a

particular species, it is necessary to identify the relevant local population which may

be impacted (Brittingham et al. 2014; Bastos et al. 2016; Brown et al. 2016). In

highly fragmented landscapes, the relevant local population may be straightforward

to delineate because of the geographical separation between the area affected by the

development and the nearest other sites where the species may be found. For

example, the presence of physical barriers (natural or anthropogenic) or long

distances between patches may limit dispersal and thus enable isolation of local

populations (e.g., natural and anthropogenic barriers for cougars, Sweanor et al.

2000; land-clearing for wombats, Walker et al. 2008; geographical distance for

sharks, Sandoval-Castillo and Beheregaray 2015). In contrast, identifying the

relevant local population may be more challenging if a species is highly mobile (and

therefore able to disperse between even geographically distant sites) or displays

migratory behaviour (e.g., between breeding sites and feeding areas), or if it is

continuously distributed across the land or seascape (DeYoung 2007).

What constitutes a “local population” is an important though often under-considered

aspect of impact assessment research. Some concept of a local population is often

implicit in considerations of spatial scale and population structure for EIA. In one

sense, the local population may simply comprise the total number of individuals that

may be affected by the proposed development or activity. In the simplest scenario,

an EIA could proceed on the basis of an estimate of the number of individuals

present in the ‘patch’ that the development or activity will affect (Total Individuals

Affected). However, it will often be desirable (or necessary) to identify the relevant

biological population that may be impacted, in the sense of a group of animals (or

‘subpopulation’) that displays some meaningful degree of genetic, demographic, or

spatial discreteness (Population Unit Affected). An effective EIA may therefore

require information about population structure, so that decision-makers can evaluate

the biological significance of potential impacts - e.g., will the development affect the

viability of a distinct population (or population unit) or is the species continuously

distributed across the impact area and its surrounds such that little or no population

structure is present? A metapopulation framework is often applied to examine

interactions between spatially distinct local populations (Levins 1969; Hill et al.

1997; Moilanen and Nieminen 2002).


60

Weak population structure is often expected for marine wildlife because of the lack

of barriers to movement and the broad distributions of many marine species (Waples

1998). Nonetheless, geographic features do exist in the marine environment that may

act as natural boundaries and thus contribute to population structuring, such as

between estuarine, coastal, and offshore habitats. Bottlenose dolphins (Tursiops

spp.), for example, are known to exhibit population (and even species) structure

across a gradient from protected inshore environments to deeper, more exposed

offshore habitats. For example, estuarine bottlenose dolphins generally exhibit

greater site fidelity and year-round residency, have stronger and more enduring

associations with conspecifics than do bottlenose dolphins in coastal habitats, and

may form distinct “communities” within particular estuaries or embayments

(Quintana-Rizzo and Wells 2001). A “community” has been defined as a set of

individuals that is behaviourally discrete from neighbouring communities and within

which most individuals associate with other members of the community (Wells et al.

1987). I suggest that a dolphin community might constitute a relevant local

population for the purposes of EIA, both in terms of comprising the total number of

animals that might be affected by a proposed development (Total Individuals

Affected) and in terms of representing a population unit of some biological

significance (Population Unit Affected). However, the diversity and flexibility of

mammalian social behaviour can make it difficult to identify communities for

dolphins and other social mammal species (both terrestrial and marine) (Cantor and

Whitehead 2013).

As nearshore environments such as embayments and estuaries are a focus point for

coastal development, EIAs will often need to consider how proposed developments

and activities will affect local dolphin populations. These environments contain

shallow and protected habitats that allow bottlenose dolphins to reside year-round

(Wells 1986; Brusa et al. 2016). Prey availability in these environments is also more

continuous and dependable (Elliott and Whitfield 2011; McCluskey et al. 2016) than

in open and coastal regions, where prey is distributed patchily and prey availability is

dictated largely by oceanic physical processes (Silva et al. 2008). The key ecological

and demographic characteristics of dolphin communities in estuaries and

embayments differ from those in coastal areas - e.g., inshore communities tend to be

small and to exhibit weak to moderate levels of dispersal and immigration (Titcomb


61

et al. 2015). These characteristics influence their vulnerability or resilience to human

impacts (Bejder et al. 2009; Pirotta et al. 2013).

Impact assessment research has been undertaken for a range of developments and

activities that may impact on dolphins, including activities such as dredging and pile

driving that may exert short-term impacts on dolphin populations (Dungan et al.

2012; Pirotta et al. 2013; Culloch et al. 2016) and those which are more enduring,

such as the construction of permanent infrastructure (Jefferson et al. 2009; Cagnazzi

et al. 2013b). The coastal and estuarine waters of Perth (Western Australia) have

experienced significant development for industrial and other commercial uses.

Notably, the Swan Canning Riverpark (SCR) estuary bisects the city, threading

through heavily developed residential and agricultural areas (Holyoake et al. 2010)

and Cockburn Sound (CS), a sheltered embayment, contains Perth’s main industrial

area. Some developments in the region have involved short-term activities (e.g., pile

driving activities conducted in the Inner Harbor of the Port of Fremantle, Salgado

Kent et al. 2012; Paiva et al. 2015), while others are continuing impacts (e.g., year-

round dredging for a shell-sand mining operation, Environmental Protection

Authority 2001). In addition, new developments may be undertaken in the near

future (e.g., a proposed outer harbor development on Kwinana Shelf, Western

Australian Planning Commission 2004; a desalination plant proposed for the

northern metropolitan coast, Mercer 2013). In many respects, these developments are

exemplars of the types of developments which may impact on dolphins and other

wildlife in coastal and estuarine environments.

In this paper, I: (a) describe a methodology to identify local populations of wildlife

and then (b) examine its further application in evaluating possible impacts of

proposed developments and activities. Firstly, I integrated social, ecological, and

genetic data collected during longitudinal field study to identify relevant local

populations of Indo-Pacific bottlenose dolphins (T. aduncus) within estuarine and

coastal waters of Perth. I employed sampling methodologies that are robust and

consistent with best-practice for long-term monitoring, abundance estimation and

behavioural study of coastal dolphins (e.g., systematic line-transect survey, photo-

identification over a four-year period (2011-2015)) to: (1) assess social networks of

bottlenose dolphins through estimates of the half-weight index (HWI) and network


62

analysis; (2) compare the role of sex composition and temporal stability of

association in driving social organisation within communities based on lagged

association rates (LAR) and preferred associations; (3) examine the spatial

segregation of the social communities by assessing home range overlaps in

conjunction with bathymetry and habitat differences, to assess the residency patterns;

and (4) evaluate the genetic relatedness within and between communities by

estimating the relatedness between individuals within and between communities.

Secondly, I considered the fine-scale population structure of bottlenose dolphins in

the context of past, current, and proposed developments for the region to

demonstrate how such information about local populations can also assist in

evaluating the possible impacts of proposed developments and activities.


3.3.1. Study area

The study area was located in the metropolitan waters of Perth (Western Australia),

one of the fastest growing capital cities in Australia (Australian Bureau of Statistics

2015). The study area encompassed 275 km2 and extended from Rockingham to

Scarborough along the coast and then inland to include part of the Swan Canning

Riverpark (SCR), an estuarine reserve (Figure 3.1). Following a mark-recapture

robust design (see Chapter 2, Chabanne et al. 2017b), the study area was subdivided

into four geographic regions with three that were defined by the topography and

bathymetry of the coastal waters (from South to North, Figure 3.1): (1) Cockburn

Sound (CS) – a semi-enclosed embayment with varying depth (< 2 to > 20 m) and

with seagrass, sand, silt, or limestone substrates; (2) Owen Anchorage (OA) – an

embayment with less than 10 m depth, except in the channel (max depth: 14.7 m),

and with a substrate mainly consisting of shell-sand and seagrass; (3) Gage Roads

(GR) – an open coastline typified by deep waters (> 10 m), with sandy beaches,

rocky reefs, and seagrass patches. The lower section of GR, also deeper (> 20 m), is

an anchoring area for ships before entering the Port of Fremantle. The SCR is a

micro-tidal estuary which encompasses an area of about 55 km2 and includes two

river systems (Swan and Canning rivers) that join near the City of Perth before

reaching the Indian Ocean through the Inner Harbour of the Port of Fremantle. While


63

the estuary is mainly shallow (< 10 m), the Inner Harbour section is maintained at 14

m through regular dredging activities (Figure 3.1b). With a Mediterranean climate,

the estuary experiences marked temperature and salinity variations through the year,

particularly when freshwater flow is weak.


64

Figure 3.1. Maps of the study area showing: (a) the transect routes per geographic regions (GR = Gage Roads, SCR = Swan Canning Riverpark,

OA = Owen Anchorage, CS = Cockburn Sound) with locations of past, current, and proposed developments (1- pile driving; 2- dredging; 3-

desalination; 4- outer harbour); and (b) the bathymetry (in meters) with the locations of the groups sighted during the systematic surveys

conducted from 2011 to 2015, including mixed groups (i.e., mix of individuals from different communities).


65

3.3.2. Data collection

Boat-based surveys were conducted between June 2011 and May 2015 and within

each season corresponding to the Australasian calendar (winter: June to August;

spring: September to November; summer: December to February; autumn: March to

May). Using boat-based photo-identification sampling, I documented individual

bottlenose dolphins based on nicks and marks on the dorsal fin (Würsig and

Jefferson 1990). Three zig-zag transect routes (offset by 2 km) were designed using

Distance 6.0 (Thomas et al. 2009) for each coastal geographic region in order to

optimise the coverage (Figure 3.1a). Each route extended to c. 7 km offshore in CS

and OA and from 5 to 3 km offshore in GR. In the SCR, I followed the same transect

route used during the 2001-03 study (Chabanne et al. 2012) which extended from the

mouth of the estuary (Inner Harbour) through the lower reaches and the main basin

where the Swan and Canning rivers join. Full details on the robust design sampling

structure design and survey methodology (i.e., predefined transect routes, Figure

3.1a) are provided in the Chapter 2 (Chabanne et al. 2017b).

A cycle was defined as a successful completion of a survey within each geographic

region of the study area. My goal was to complete a cycle in a minimum period,

although a minimum of two days was required because of daylight. During a survey,

a sighting was defined as a group of dolphins observed within c. 250 m on either side

of the boat along the transect route (Wells et al. 1987; Wells et al. 1999; Quintana-

Rizzo and Wells 2001). A group consisted of one to several dolphins. Dolphins were

considered to be in a group if they were within 10 m of any individual within the

group (a 10 m “chain” rule) and engaged in the same behaviour (Smolker et al.

1992). For each sighting, I recorded the location (southing/easting using a hand-held

GPS unit), behaviour, group size, and age-sex composition. Age classes (adult,

juvenile, and calf) were based on the body size or the presence of a dependent calf.

Individuals were sexed through: (i) molecular analyses of tissue samples (Gilson et

al. 1998; Brown et al. 2014) collected via remote biopsy sampling (Krützen et al.

2002); (ii) field observation of the genital regions; or (iii) the presence of a

dependent calf (for females). I performed photo-identification using a Nikon D300

with Nikkor lens 70-300 mm or a D7000 with Nikkor lens 80-400 mm. Full details


66

for photo-identification and grading processes are provided in Chapter 2 (Chabanne

et al. 2017b).

3.3.3. Association patterns

To analyse patterns of association, I used only high-quality photographic

identifications, from groups for which all individuals were identified. I did not

consider the distinctiveness of the individuals to avoid small sample sizes. The

survey frequency also allowed for use of temporary marks, if required. Individuals

present in the same group were assumed to be associated ("gambit of the group",

Whitehead 2008a). Calves were excluded from the analysis because they lack

identifying marks, are dependent on their mothers and have high natural mortality

(Mann et al. 2000). The sampling period was set to one day to minimise sampling

time incoherency between successful surveys of the entire study area (i.e., a cycle).

Individuals seen multiple times within the same cycle were restricted to the first

sighting only. The strength of associations among dyads (i.e., pairs of individuals, n

= 8,256) was calculated using the half-weight index (HWI, Cairns and Schwager

1987). Values of HWI range from 0 (never associated) to 1 (always associated). The

HWI is frequently used in social structure of cetaceans as it reduces bias due to

incomplete identification within encounters (Cairns and Schwager 1987). Only

adults and juveniles sighted more than five times over the entire study period were

retained for this analysis. The minimum number of sightings was decided by

comparing the social differentiation (S, measure of variability of the associations)

and Pearson’s correlation coefficient (r, measure of the quality of the representation

of the association pattern, Whitehead 2008b) for different sets of data based on a

minimum number of sightings per individual, until appropriate values were reached.

Specifically, an S < 0.3 indicates that the society is homogeneous, 0.5 < S < 2

indicates that the society shows some strong associations between individuals, and S

> 2 indicates that the society generally has weak associations between individuals

(Whitehead 2008b). Additionally, an r value near 1 indicates that the representation

is excellent, while r ~ 0.8 suggests a good representation and r ~ 0.4 indicates a

moderate representation (Whitehead 2008b). All association patterns were analysed

using the software SOCPROG 2.6 (Whitehead 2009).


67

A Monte Carlo permutation test was conducted to examine whether associations

within the study area population were different from random (Bejder et al. 1998;

Whitehead et al. 2005; Whitehead 2008a). As such, higher coefficients of variation

(CV) of real association indices compared to that of randomly permuted data

indicated the presence of preferred long-term companions in the studied population

(Whitehead 1999). I ran 103 permutations with 10

3 flips per permutation for the

complete dataset and significant variations from random were tested using a two-

tailed test (P-value = 0.05). The number of permutations was determined to be

sufficient when the P-value stabilised (Bejder et al. 1998). The preferred

associations are those for which an association index value is at least twice higher

than the mean (Whitehead 2008b). A Mantel test, using 103 permutations, was

carried out to examine whether differences in associations occurred between sex

classes (two-tailed 0.05 P-value, Schnell et al. 1985).

3.3.4. Community structure and dynamic

To investigate the social structure based on the HWI, I calculated the eigenvector

modularity network algorithms to identify cut-off limits to identify possible

communities (Newman 2004, 2006). A modularity M > 0.3 indicated that the

community division is meaningful (Newman 2004; Whitehead 2009). I used the

software NetDraw 2.139 (Borgatti 2002) to visualise the network structure. For

comparison, I carried out an average linkage hierarchical cluster analysis that

calculated a cophenetic correlation coefficient (CCC). A CCC > 0.8 indicates a good

match between the degree of association between individuals and the association

matrix (Bridge 1993).

I examined the association levels, some network measures, sex segregation and the

temporal stability of associations to highlight potential differences in association

patterns between the different communities identified. First, mean and maximum

levels of associations were compared. Second, I measured the network strength,

clustering coefficient and affinity within communities. The strength is the sum of the

association indices of each individual, also defined as a measure of gregariousness

(Barrat et al. 2004); the clustering coefficient indicates how well an individual’s

associates are themselves associated; and the affinity is a measure of the strength of


68

an individual’s associates (Whitehead 2016). All the network measures were

calculated in SOCPROG 2.6 (Whitehead 2009). Third, a Mantel test (as described

above) was then carried out to examine whether differences in associations occur

between sex classes within each community. Additionally, tests for preferred or

avoided associations were run for each community and per sex classes as described

above. And finally, I measured the persistence of associations within each

community by calculating the lagged association rates (LAR, Whitehead 1995). The

LAR estimates the probability that two individuals sighted together at a given time

will still be associated at some time lag later. LARs from each community were

compared to the null LAR of the complete dataset (i.e. association value the animals

would have if associating randomly, Whitehead 1995). I then tested exponential

decay models characterising the patterns of dyadic association over time. The quasi-

Akaike information criterion (QAIC) was used for model selection (Whitehead

2007). I used the jackknife method to obtain estimates of precision of the LAR

(Efron and Stein 1981). LARs were also estimated and modelled as above for each

sex class within each community.

3.3.5. Spatial distribution of communities

I calculated the estimates of kernel density (KDE) in ArcGIS 10.3 and estimated the

probability of contours of 50% (i.e., the core of a community) and 95% (i.e.,

community’s home range defined by the outermost boundaries) by pooling sightings

of individuals assigned to the same community. Individuals that were equally

observed in two or more geographic regions were excluded from this analysis. I used

the kernel interpolation with barriers tool to take into account land barriers to

movements (the output grid cell size was set to 200 × 200m and the bandwidth was

fixed to 6,000 for each individual, Sprogis et al. 2015). All other steps followed the

protocols by MacLeod (2014) and were calculated in the Universal Transverse

Mercator (UTM) Zone 50 South projection using the coordinate system World

Geodetic System (WGS) 1984 datum. Overlaps home ranges between each

community were computed using the Intersect tool in ArcGIS. In order to

characterise some factors associated with community structure, I also calculated an

asymmetric matrix of pairwise individual home range (95% kernel density) overlaps

following the same protocol as above and conducted a Mantel test (103 permutations)


69

to check the correlation between home range overlaps and HWIs for each individual

pairs between and within communities.

3.3.6. Residence time

Using the software SOCPROG 2.6 (Whitehead 2009), I assessed the demographic

processes within each community by estimating the lagged identification rates (LIR)

for each individual within their respective assigned community (Whitehead 2001).

This analysis estimated the probability that an individual would be resighted in the

study area after a certain time lag (td) in comparison to a randomly chosen

individual. I then fitted different models of no movment (i.e., closed populations),

emigration and reimmigration, and emigration, reimmigration and mortality to the

observed LIR (Whitehead 2001). I used the QAICc to select the most parsimonious

model (Burnham and Anderson 2002). The LIR confidence intervals (CI) were

obtained using bootstrap replicates (Whitehead 2008b).

3.3.7. Genetic relatedness

Skin samples were collected via remote biopsy sampling (Krützen et al. 2002) over

the four-year period (2011-2015) mentioned earlier. However, additional samples

collected between 2007 and 2010 were also included for genetic testing. All biopsy

samples were stored in DMSO buffer for cryopreservation. Genomic DNA was

extracted from all skin samples using the Gentra Puregene Tissue Kit (Qiagen) and

following the manufacturer’s protocol. Samples were genotyped at 13 different

microsatellite loci: DIrFCB4, DIrFCB5 (Buchanan et al. 1996), LobsDi_7.1,

LobsDi_9, LobsDi_19, LobsDi_21, LobsDi_24, LobsDi_39 (Cassens et al. 2005),

SCA9, SCA22, SCA27 (Chen and Yang 2008), TexVet5, TexVet7 (Rooney et al.

1999). I followed the PCR conditions as described in Frère et al. (2010a). The single

stranded PCR products were run on an ABI 3730 DNA Sequencer (Applied

Biosystems). Genotypes were scored using GENEIOUS 9.1.5

(http://www.geneious.com; Kearse et al. 2012) with microsatellite plugin 1.4

(Biomatters Ltd). Each microsatellite locus was checked for null alleles† and scoring

errors using the software Micro-Checker 2.2.3 with a confidence level of 95% (Van

Oosterhout et al. 2004). Departures from Hardy-Weinberg equilibrium (HWE) and


70

linkage disequilibrium† were tested using the Markov chain probability test and 104

iterations in Genepop 4.4.3 (Rousset 2008). Significance values for multiple

comparisons were adjusted by sequential Bonferroni corrections (Rice 1989). I

calculated individual pairwise relatedness within and between social communities

using Queller and Goodnight (1989) index (QG) in Coancestry 1.0.1.2 (Wang 2011).

Average relatedness coefficients within communities were tested using t-test. I also

conducted an ANOVA test to identify the correlation between pairwise relatedness

and HWI within each community.

3.4. Results

3.4.1. Effort and group size

A total of 322 group sightings were successfully (i.e., all individuals identified from

good quality photos) obtained during the four-year (2011-2015) study period. In

total, 315 individual dolphins (excluding calves) were identified.

Average group size was 5 (SE 0.27) individuals (range: 1-31 individuals) (Table

3.1). Although group size was similar across the three coastal geographic regions, it

was smaller in SCR, with an average of four individuals and a maximum group size

of 14 individuals.

Table 3.1. Number of groups and group size (mean (SE), minimum and maximum)

by geographic region.

# Groups Group size

Mean (SE) Min - Max

Overall 323 5 (0.27) 1 - 31

GR 44 7 (0.75) 1 - 31

SCR 107 4 (0.48) 1 - 14

OA 77 6 (0.56) 1 - 24

CS 95 7 (0.51) 1 - 27

Note: GR = Gage Roads, SCR = Swan Canning Riverpark, OA = Owen Anchorage,

CS = Cockburn Sound.


71

3.4.2. Community structure and dynamic

After restricting the dataset to those individuals with at least five sightings, 129

individuals were identified (n = 57 females, 44 males, and 28 unsexed). Both

community division using the eigenvector method of Newman (2006) and

modularity from gregariousness and hierarchical clustering using average linkage

methods indicated a meaningful community division with maximum modularity of

0.514 and 0.526 for an HWI of 0.022 and a cophenetic correlation coefficient (CCC)

of 0.843, indicating a good match between the degree of association between

individuals and the association matrix. Both methods assigned individuals to four

communities, although one community was split into two sub-communities (D and

D’). From here on, I refer to these communities as ComA, ComB, ComC and ComD.

Three individuals (designated as KWL, GIL, MUF, n = 2.3%) were assigned in

different communities depending on the method (Figure 3.2 and see Appendix

A3.1). Therefore, we used their respective sighting locations to assign them to one

community only (KWL in ComB; GIL and MUF both in ComA).


72

Figure 3.2. Network diagram for 129 bottlenose dolphins using the HWI. The shape

of each node indicates its sex (circle: females; square: males; triangle; unsexed), and

the colour of each node indicates its unit defined by the modularity of Newman

(2006) (purple ComA; red ComB; green ComC; yellow and orange sub-communities

D and D’), although three individuals were assigned to two different units depending

on the method (Newman vs. Hierarchical linkage). Only links representing

affiliations (HWI > 0.16) are shown, and link width is proportional to index weight.

Node size is based on the betweenness centrality measure of each individual.

3.4.3. Association patterns

The overall mean HWI and maximum HWI were 0.05 (SE 0.02) and 0.55 (SE 0.19),

respectively. The coefficient of correlation (r) between the true and estimated

association indices for the entire study population indicated a moderate

representation of the data (r = 0.476, SE 0.024) and a well-differentiated society

value (S = 1.020, SE 0.033) suggesting that some individuals form strong

associations. I ran this analysis within each community (identified by the network


73

and community analysis) and found values of r higher than 0.4 indicating that my

analysis is representative of the true patterns (Table 3.2).

Table 3.2. The mean association indices for the population overall and per

community (ComA, ComB, ComC and ComD); the measure of social differentiation

(S); and the correlation coefficient of the true and estimated association matrices (r).

n

restricted*

Mean

HWI

Maximum

HWI S r

Overall 129 0.05 (0.02) 0.55 (0.19) 1.020 (0.033) 0.476 (0.024)

ComA 15 0.21 (0.08) 0.59 (0.20) 0.662 (0.090) 0.653 (0.053)

ComB 25 0.17 (0.05) 0.61 (0.24) 0.753 (0.062) 0.742 (0.027)

ComC 36 0.19 (0.08) 0.56 (0.16) 0.683 (0.069) 0.712 (0.048)

ComD 53 0.13 (0.05) 0.51 (0.16) 0.567 (0.073) 0.577 (0.050)

Note: Numbers between brackets () are the standard errors of the respective

parameters. *Individuals seen at least five times. Some individuals could not be

assigned to a community.

Tests of preferred/avoided associations showed a significantly higher CV of

observed vs. expected association indices (HWIobserved CV = 2.1405, HWIrandom CV =

1.8797, P-value < 0.001) indicating that long-term preferred companions are present

in the overall population. The proportion of non-zero association indices was

significantly lower in the observed vs. the expected association indices (observed =

0.2648, random = 0.3069, P-value < 0.001) indicating avoidance between some

individuals in the population. More specifically, individuals were more likely to

associate with same-sex individuals than among individuals of different sex (Mantel

test, HWIwithin = 0.07, SE 0.03; HWIbetween = 0.05, SE 0.02, P-value < 0.001), with

preferred associations occurring between females (HWIobserved CV = 1.9989;

HWIrandom CV = 1.7675, P-value < 0.001) and between males (HWIobserved CV =

2.1770; HWIrandom CV = 1.9222, P-value < 0.0001).

The mean HWI was lower in ComD (0.13, SE 0.05) and higher in ComA (0.21, SE

0.08), although stronger dyads were estimated in ComB (maximum HWI = 0.61, SE

0.24) (Table 3.2). Mantel test confirmed that associations were stronger within than

between communities (HWImean, within = 0.16, SE 0.07; HWImean, between = 0.01, SE


74

0.01, P-value < 0.0001) and individuals were more likely to associate with same-sex

individuals than among individuals of different sex in all communities to the

exception of ComA (Table 3.3).

Table 3.3. Association indices within and among sex classes (Mantel test, 0.05 one-

side).

HWI mean (SE) P-value

Within Between

Overall 0.07 (0.03) 0.05 (0.02) < 0.001

ComA 0.20 (0.15) 0.20 (0.05) 0.270

ComB 0.21 (0.07) 0.13 (0.05) < 0.001

ComC 0.23 (0.09) 0.16 (0.08) < 0.001

ComD 0.15 (0.06) 0.12 (0.06) 0.008

Significant differences between communities were found in the network measures

with ComC and ComD communities having higher strength and affinity (Table 3.4

and see Appendix A3.2 for Mann-Whitney test, P-value < 0.02), although the

strength was much higher for ComA community when considering all individuals

(i.e., including individuals seen less than five times, see Appendix A3.3). ComA had

the highest clustering coefficient; however, this may be biased by the small number

of individuals seen more than five times (n = 15), which may result in individuals

appearing more connected than they actually are. ComC had the next highest

clustering coefficient, indicating a dense network of individuals in that community.

Preferred associations occurred in all communities with all CVs of association

indices higher in the observed vs. the random values (Table 3.5). Specifically,

preferences occurred between females within ComB, ComC and ComD and between

males within ComB and ComD. Although avoidances occurred with the proportions

of non-zero indices being lower in the observed vs. the random values, they were not

sex-specific in ComC as found in ComD or for males in ComB (Table 3.5).


75

Table 3.4. Average strength, clustering coefficients, and affinity (SE) with

comparisons from random calculating using half-weight indices for individuals

sighted at least five times.

Strength Clustering Affinity

coefficient

ComA (n=15)

Mean 3.73 (1.11)* 0.25 (0.11) 4.88 (0.90)

Random 3.51 (1.11) 0.18 (0.04) 5.14 (0.91)

ComB (n=25)

Mean 5.01 (1.43)* 0.19 (0.05) 5.83 (0.97)

Random 5.08 (1.50) 0.17 (0.04) 6.04 (0.81)

ComC (n=36)

Mean 7.91 (2.94) 0.22 (0.05)* 8.49 (0.78)

Random 7.90 (2.93) 0.20 (0.06) 8.59 (0.55)

ComD (n=53)

Mean 7.31 (2.73)* 0.19 (0.04) 8.01 (0.86)

Random 7.26 (2.78) 0.16 (0.03) 8.02 (0.53)

Note: n = number of samples; * Significant differences from 103 random networks:

P-value < 0.05.


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Table 3.5. Sex class (female, male, unknown sex) permutation tests for preferred

(HWI CV) and avoided (proportion of non-zero indices) associations for the

population overall and per community.

HWI CV Proportion of non-zero indices

n

a Observed Random P-value

Observed Random P-value

Overall

All 129 2.14054 1.87585 **

0.26478 0.30717 **

Females 57 1.99886 1.76774 **

0.27318 0.32263 **

Males 44 2.17702 1.92031 **

0.29704 0.33345 **

Unknown 28 1.88829 1.84766 **

0.30423 0.31697 **

ComA

All 15 0.95857 0.90723 *

0.70476 0.72067 NS

Females 5(2)

- - -

- - -

Males 2(2)

- - - - - - -

Unknown 8(2)

- - -

- - -

ComB

All 25 0.99969 0.78870 **

0.83333 0.85316 *

Females 13 0.74870 0.68623 **

0.91026 0.89579 NS

Males 12 1.18034 0.98124 ***

0.74242 0.81982 ***

Unknown 0 - - -

- - -

ComC

All 36 0.93709 0.88541 ***

0.73333 0.75045 **

Females 14 0.80479 0.76055 **

0.7923 0.77871 NS

Males 11 0.72803 0.72823 NS

0.81818 0.81818 NS

Unknown 11 0.83146 0.83155 NS

0.78182 0.77996 NS

ComD

All 53 1.06854 0.99465 **

0.60958 0.63421 ***

Females 25 0.96490 0.93202 **

0.64000 0.66447 **

Males 19 1.20840 1.04314 ***

0.63158 0.68587 ***

Unknown 9b - - - - - -

Note: n = number of individuals; NS = non-significant; *P-value < 0.05; ** P-value

< 0.01; *** P-value < 0.001. a Individuals seen at least five times.

b Test could not be

run because of degenerate matrix.


77

Lagged association rates (LAR) for each community were higher than the null LARs

for the overall population indicating that associations within communities were

relatively stable and non-random over the study period (Figure 3.3). The most

parsimonious LAR model (based on the QAICc, see Appendix A3.4) showed

constant companions for all communities and brief associations described as rapid or

casual but lasting for less than a day. However, the casual acquaintances in ComB

lasted for only a few days.

Figure 3.3. Lagged association rates (LAR) for all individuals (black line) and

within the communities (ComA purple; ComB red; ComC green; and ComD

yellow). The null association rate (dash lines) and jackknife error bars are shown.

Female and male LARs (except in ComA) were higher than their respective null

LARs, particularly in ComB, indicating that associations between individuals of the

same sex were relatively stable over the study period within their respective

communities (Figure 3.4). LARs of males were generally higher than the LARs of

females indicating that associations between males were stronger than associations

between females, although more females were identified in ComC and ComD (sex

ratio 0.71:1 and 0.76:1, respectively). Female and male LARs for ComB were higher

than LARs of other communities indicating that associations within ComB were

higher than in other communities (this related only to sexed individuals, Figure 3.4).


78

Figure 3.4. Lagged association rates (LAR) for males and females (faint colour)

bottlenose dolphins of each community ((a)-ComB, (b)-ComC and (c)-ComD). The

null association rate (dash lines) and jackknife error bars are shown. Note that the

LAR for ComA could not be estimated because of small sample sizes.


79

The most parsimonious LAR models (based on the QAICc, see Appendix A3.4) for

each sex class and per community suggested some long lasting associations and

others that were of brief duration because of constant companions and rapid

dissociations or casual acquaintances lasting less than a day. However, if not

constant, female and male associations in ComB still lasted for up to month or few

days, respectively, before dissociating (Appendix A3.4).

3.4.4. Spatial distribution of communities

I estimated core areas (50% kernel density) and home ranges (95% kernel density) of

each of the four communities using individuals assigned to each respective

community. Core area estimates were similar when using individuals seen at least

five times and when including all individuals irrespective of their sighting frequency.

Similarly, home range estimates were similar when using individuals seen at least

five times and when including all individuals irrespective of their sighting frequency

(see Appendix A3.5). As such, I have only presented the results using all clustered

individuals. The core areas (i.e., 50% kernel density estimated using all individual

sightings) of each community were discrete and located in each geographic region

(Figure 3.5a) with sizes varying from 6.83 km2 (ComB) to 31.05 km

2 (ComC)

(Appendix A3.5). The core area of ComB mainly covered shallow waters (83%

coverage at < 10 m) while the core area of ComA was mainly in deep water (71%

coverage at > 10 m) (see Appendix A3.6). Home ranges were mainly contained

within the respective geographic region (Figure 3.5b), with the home range of ComB

mainly covering the shallow waters of the SCR (61.4% coverage at < 10 m).

Conversely, the home range of ComA covered much of the GR region, which is

mainly deeper waters (55.4% coverage > 15 m). I therefore referred each community

to a geographic region, namely ComA to GR; ComB to SCR; ComC to OA; and

ComD to CS. Seven individuals (seen less than five times) were not assigned to a

community because I could not define the geographic region where they were mainly

sighted.

Permutation tests indicated significant avoidance between communities in GR, SCR,

and CS, but occurrence of some associations with ComC in OA. However, when

tested with all the individuals (including individuals seen less than five times),


80

avoidance tests were all non-significant. There were overlapping home ranges

between each of the communities (Figure 3.5b) with 17% (n = 54 groups) of the

groups being composed of individuals from different communities. Most of those

multi-community groups (78%) were observed in OA and the lower reach section of

SCR (Figure 3.1b). The smallest home range overlap occurred between ComA and

ComD (2.42 – 7.96 km2) and the largest between ComC and ComD (43.97 – 45.62

km2) (Appendix A3.8). Additionally, 30 and 32% of ComC and ComD home ranges,

respectively, overlapped with the core area of ComB, covering the entire Inner

Harbour of the Port of Fremantle.

Inspection of the overlap in home ranges for individuals seen at least five times and

assigned to a community indicated that there was a clear difference of percentage of

overlap from dyads within and between communities, with much higher proportion

of dyads sharing > 80% of the home range within communities (18, 62, 47 and 36%

for ComA, ComB, ComC and ComD communities, respectively) than between (only

1.1% of the dyads showed that > 80% of the home range was shared, although this

was not necessarily the case for both individuals of the dyad).

Home range overlap significantly explained the HWI dyads at the population-level

(R = 0.10, P-value < 0.01) and more specifically for dyads allocated to ComA and

ComD communities (R ComA = 0.07, R ComD = 0.07, P-value < 0.01). Measures of

HWI dyads for individuals allocated to ComB and ComC were not explained by

their home range overlap (P-value > 0.05).


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Figure 3.5. Study area showing the bathymetry and core areas (a) based on the 50% kernel density and home ranges (b) based on the 95% kernel

density estimated for each community using clustered individual sightings. Communities are: ComA-purple; ComB-red; ComC-green; and

ComD-yellow.


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3.4.5. Residence time

I examined the residency patterns of all individuals (including individuals seen less

than five times) per community. Models consisting of parameters indicating the

occurrence of emigration and mortality best fitted the LIR of each of the four

communities (based on the QAIC, Appendix A3.10). Parameters showed differences

in community sizes and residency. In ComB, 78% of the individuals were described

as residing for nearly 18 years (95% CI 10-87 years). Conversely, 58% of the

individuals observed in ComA were described as individuals staying for maximum c.

five years (95% CI 3-11 years). Another LIR model represented the demography of

ComA best (i.e., emigration, reimmigration and mortality, ΔQAICc ≤ 2) and showed

that a minority of individuals that were considered as a community (n = 20%) also

spent more time outside the area than in (Appendix A3.10). Most of the individuals

in ComC and ComD (n = 63 and 66%, respectively) were described with a long

residence, with individuals from ComC staying for 7.5 years (95% CI 4-23 years)

and ComD individuals for about 12 years (95% CI 7-47 years).

3.4.6. Genetic relatedness

A total of 107 tissue biopsy samples were collected for genetic analyses. Samples

were from individuals identified during the current study and who had been assigned

to a community, and were checked for duplicates and removed when allele

frequencies were missing for more than four loci. In addition, three loci were

removed for further genetic analyses because of scoring errors due to stuttering

(SCA27) or because of departure of HWE associated with homozygosity excess (i.e.,

frequencies of null alleles > 0.05 for TexVet5 and SCA17).

I identified 35 pairs of high relatedness values (QG > 0.5), although I didn’t have

prior information on their relatives for most of these. While most pairs (n = 20) were

of individuals assigned to the same communities, others were identified from

individuals assigned to different communities, although no pairs involved

individuals from ComA and ComD. The bootstrap values of within-community

pairwise genetic relatedness coefficients averaged 0.012 (SE 0.005, 95% CI 0.007-


83

0.017) over the four communities, whereas the average pairwise genetic relatedness

coefficient of the population was -0.008 (SE 0.002, 95% CI -0.011, -0.006). All

communities except ComD had positive mean genetic relatedness coefficient (Table

3.6) and individuals were more related to individuals from the same community than

different communities. However, there were no significant differences in relatedness

within and between ComA and ComC (t-test, P-value > 0.05, Table 3.6). Significant

correlation between pairwise relatedness coefficients and HWI was found in ComB

(ANOVA, P-value < 0.05, Table 3.7), although the correlation was small (R = 0.01).


84

Table 3.6. Bootstrap mean (and standard error) genetic relatedness (Queller and Goodnight, 1989) for within-community (bold) and with

individuals from other communities of bottlenose dolphins (between-community) genotyped with ten microsatellite loci in Perth metropolitan

waters, WA.

ComA ComB ComC ComD

ComA 0.048 (0.020)

0.014 (0.098) *** 0.025 (0.010) NS -0.027 (0.007) *

ComB 0.014 (0.098) NS 0.063 (0.012)

0.003 (0.008) * -0.026 (0.005) *

ComC 0.025 (0.010) NS 0.003 (0.008) *** 0.037 (0.036)

-0.030 (0.006) *

ComD -0.027 (0.007) *** -0.026 (0.005) *** -0.030 (0.006) *** -0.012 (0.002)

Note: t-tests were performed per column to compare within-community mean to between-community means: *P-value < 0.05;

**P-value < 0.01;

***P-value < 0.0001); NS = non-significant.

Table 3.7. Correlation coefficient R and ANOVA test between relatedness coefficient and HWI pairwise within-community.

Correlation R P-value

(ANOVA test)

ComA -0.0050 0.43

ComB 0.0115 0.04*

ComC -0.0005 0.35

ComD 0.0009 0.17

Note: * Significant P-value < 0.05.


85

3.5. Discussion

It is imperative to characterise the fine-scale population structure of mobile,

continuously distributed species so that an EIA is conducted within an appropriate

biological context. The first step in that process is to identify the relevant local

wildlife populations that will be affected by a proposed development or activity. This

study demonstrated that social, spatial, ecological, and genetic information may be

used to identify local communities of bottlenose dolphins.

Analyses of association patterns of photo-identified bottlenose dolphins revealed

four distinct social mixed-sex communities in which patterns of social organisation,

social dynamics, home ranges, and residency differed (see Table 3.8 for a summary).

Spatial analyses found these communities occupy discrete core areas associated with

different environmental and bathymetric characteristics. As expected, high site

fidelity and residency patterns were documented for communities occupying

shallow, protected embayment and estuary habitats. Overlap in home ranges (e.g.,

dyads within ComA-GR and ComD-CS) and genetic relatedness (e.g., dyads within

SCR community) explained associations between some dyads. However, overall,

such factors explained only little of the associations between individuals, suggesting

that other explanatory factors drive community structure at a local scale.


86

Table 3.8. Summary comparison of social, temporal, spatial, residency and genetic

patterns across the four communities (ComA, ComB, ComC and ComD). Social

differentiation is described using measures of strength (i.e., measure of

gregariousness); the clustering coefficient (i.e., degree of connection between

associates); and affinity (i.e., the strength of the associates).

Parameters ComA ComB ComC ComD

Group size Large Small Large Large

Population size Large Small Medium Medium

Social network measures

Strength Weak Medium Strong Strong

Eigenvector centrality Weak Weak Strong Medium

Reach Weak Medium Strong Strong

Clustering coefficient Strong Medium Strong Medium

Affinity Weak Medium Strong Strong

Temporal associations

LAR index High

Stable

High

Stable

Medium

Stable

Medium

Stable

Maximum duration of

shortest associations < day days-month < day < day

Spatial distribution

Core area - Home range GR SCR OA CS

Water depth Deep Shallow Mixed Mixed

Residency

Site fidelity Weak Strong Strong Strong

Duration Short-term Very long-

term Long-term Long-term

Status Transient Resident Resident Resident

Genetic relatedness a

Kin-selection Weak Weak Weak No kin-

selection

Note: a kin-selection measurement should be used with caution because of the

limited number of individuals genetically sampled in ComA, ComC and ComD.

GR = Gage Roads, SCR = Swan Canning Riverpark, OA = Owen Anchorage, CS

= Cockburn Sound.


87

3.5.1. Community segregation

Dolphins exhibited a complex structure of associations across the coastal and

estuarine seascape of the study area. Network analysis identified four social mixed-

sex communities which have few interactions between them. While some constant

companionship relationships were identified in all communities, each community is

driven by casual acquaintance relationships demonstrating rapid disassociation and

frequent re-association (Wells et al. 1987). However, even for constant companions,

the shortest associations between individuals within ComB (i.e., SCR community)

lasted for a few days to a month between females, suggesting stronger maternal

cooperation (Wells et al. 1987; Lusseau et al. 2003) in the estuary. In fact, the entire

ComB (females and males) presented a much higher degree of stability (LAR) than

occurred in any of the other communities; this may reflect the ecological constraints

associated with estuarine systems (Lusseau et al. 2003) and human pressures.

Social segregation of dolphin populations within nearshore and inshore habitats has

been documented in Indo-Pacific bottlenose dolphin elsewhere. For example,

discrete communities occur in the Port Stephens embayment in south-eastern

Australia, with two communities occupying spatially discrete core areas within the

embayment (Wiszniewski et al. 2009). Here, despite some home ranges overlapping

(95% KDE), each community was associated with a distinct geographic region

within the study area, namely: an estuary (SCR – ComB), a semi-enclosed

embayment (OA – ComC and CS – ComD), and an open coastline (GR – ComA).

The core areas (50% KDE) of the communities did not overlap, suggesting that

ecological differences among communities reflect environmental differences in

bathymetry, benthic substrate, habitat types, as well as human impacts.

Communities also differed in their sociality. Titcomb et al. (2015) demonstrated that

habitat shape could have an effect on the structure and association patterns within

communities, by influencing movement patterns and encounters between

conspecifics. Here, the densest communities in the network, OA (ComC) and CS

(ComD) (i.e., indicated by higher strength and affinity), were found in a semi-

enclosed embayment. As the SCR community (ComB) occupies an estuary with

narrow channels, encounters between conspecifics may occur on a daily basis.


88

However, the mean sociality for that community was not as high, which may reflect

the limited number of possible associates given its small size (n = 25, excluding two

individuals seen less than five times). Conversely, the GR community (ComA) was

the least cohesive, with high redundancy in connection expressed by lower strength,

which is consistent with individuals occupying larger ranges and, consequently, less

frequent encounters with conspecifics.

The LIR analysis identified clear differences in site fidelity and residency pattern

between communities. These differences may be related to the difference in habitat

structure and prey distribution between open coastline (GR) and more protected

embayment and estuary (OA & CS and SCR) habitats. Individuals in open coastlines

often have diminished levels of site fidelity and a more extensive home range

(Defran and Weller 1999; Oudejans et al. 2015; Sprogis et al. 2015). Here,

individuals from GR community (ComA) showed no residency pattern, with most

identified individuals seen less than five times and LIR models indicating that

individuals spent more time outside the study area. In addition, the large but variable

estimates of abundance (see Chapter 2, Chabanne et al. 2017b) suggested that

individuals identified in this geographic region (GR) may be members of a larger

population located further north. In contrast, residency period was estimated to be

more than seven years in OA and CS and 18 years in SCR, which is consistent with

other studies indicating that dolphins occupying shallow and protected areas show a

high degree of residency and long-term site fidelity (Wells 1986; Sprogis et al. 2015;

Brusa et al. 2016) and often belong to relatively small and stable communities

(Wells et al. 1987). That latter characteristic is consistent with the small size of the

resident communities estimated by the LIR model (SCR ≈ 21 residents; OA ≈ 43;

and CS ≈ 64) and the abundances estimated via mark-recapture analyses (SCR ≈ 19

individuals and CS/OA (combined) ≈ 122; see Chapter 2, Chabanne et al. 2017b).

While habitat differences seemed to largely account for association differences,

range overlap only weakly predicted the association strength of the two communities

at the northern and southern extremes (GR and CS). Likewise, genetic relatedness

only explained associations within the SCR community. Kin-based and overlapping

associations have been recorded in some bottlenose dolphin populations (Parsons et

al. 2003; Frère et al. 2010b), but not in others (Möller 2001). It would therefore


89

appear that other intrinsic or extrinsic factors are likely to drive the social patterns

within each community. The KDE method used to calculate the habitat used by each

individual is also limited by the sample size (i.e., number of observations per

individual). Sprogis et al. (2015), for example, calculated the KDE for individuals

seen at least 30 times (Seaman et al. 1999), although other studies indicated that 100-

300 observations per individual were necessary to obtain highly precise

representation of space use (Girard et al. 2002).

3.5.2. Using social, ecological, and genetic data to evaluate the impact of

developments and activities on the relevant local population: four case studies

Once the relevant local wildlife populations have been identified, social, spatial,

ecological, and genetic information about those local populations can then be used to

evaluate potential environmental impacts on those populations. The range of impacts

that may affect bottlenose dolphins in coastal and estuarine habitats include: habitat

degradation, indirect and direct interactions with commercial and recreational

fisheries, vessel disturbance, and environmental contaminants (e.g. Dungan et al.

2012; Pirotta et al. 2013; Todd et al. 2015; Culloch et al. 2016).

To demonstrate the utility of such information for evaluating environmental impacts,

I consider four case-study examples of developments or activities that may affect

dolphins in the context of one of the local communities identified in this study: pile

driving (SCR), dredging of seagrass (OA), operation of a desalination plant (GR),

and construction of a large harbour (CS).

3.5.2.1. Pile driving in Swan Canning Riverpark (SCR)

Pile driving involves the use of large hammer mounted on a crane to drive piles into

the seabed. The process may affect marine mammals by masking underwater sounds,

causing behavioural changes (e.g., avoidance), or causing hearing damage or

physiological injury (David 2006; Brandt et al. 2011; Erbe 2013).

Paiva et al. (2015) observed a decrease in detection of bottlenose dolphins during

pile driving activities in the Inner Harbour at Fremantle (located at the entrance to


90

SCR), and suggested that dolphins may have been using other areas during periods

of pile driving activity. If such avoidance or displacement behaviour occurs, then

pile driving may affect dolphins in two relevant ways: (a) a decline or cessation in

foraging activity within the harbour, which is a known foraging habitat (Chabanne et

al. 2012) and (b) a decline or cessation in the use of the harbour to transit between

the estuary and adjacent coastal waters. The latter impact is particularly significant

as the harbour is the only access way between the SCR and the adjacent coastal

waters. Were movements of dolphins through the harbour to cease or greatly

diminish over an extended period of time, then demographic isolation of the SCR

community might occur.

Several characteristics of the SCR community make it relatively more vulnerable to

long-term demographic isolation and would make local extinction a plausible risk,

including: (1) small community size; (2) a recent disease-related mass mortality

event (Holyoake et al. 2010); (3) injury and mortality from fishing line

entanglement; and (4) exposure to high levels of boat traffic and to occasional

harassment.

3.5.2.2. Dredging in Owen Anchorage (OA)

Dredging of marine habitats may occur to create or maintain infrastructure (e.g.,

shipping channels) or to remove benthic material such as shellsand for commercial

purposes. Todd et al. (2015) reviewed the effects of marine dredging activities on

marine mammals and concluded that (a) direct impacts (such as vessel collisions and

underwater noise emissions) were unlikely because vessel speeds were slow and (b)

the low-frequency levels (below 1 kHz) emitted by dredgers should not cause

damage to marine mammal auditory systems. However, underwater noise from

dredging may mask prey sounds and dolphin vocalisations and lead to displacement

(Pirotta et al. 2013), particularly if activities directly impact on marine mammals

prey species (Todd et al. 2015). Dolphins may also be attracted to dredging sites if

the disturbance facilitates the capture of fish (e.g., Chilvers and Corkeron 2001).

A long-term shellsand dredging operation operates in Owen Anchorage which relies

on dredging of suitable substrates (Environmental Protection Authority 2001; BMT


91

Oceania 2014). The extensive coverage of shallow (< 10 m) sand areas and seagrass

meadows (BMT Oceania 2014) sheltered from the oceanic swell in OA means the

area is likely to support a broad assemblage of prey species for dolphins (Kendrick et

al. 2000; Heithaus and Dill 2002; Hyndes et al. 2003; Finn 2005; Sampey et al.

2011). The current management plan for the dredging operation, developed to meet

the requirements of approval conditions imposed in 2002 after an EIA of the

operation, focuses on the dredging of areas devoid of seagrass to minimise

environmental impacts to benthic habitats and fisheries (BMT Oceania 2014).

The focus on dredging of non-seagrass areas and the overall scale of the dredging

operation suggest that impacts on prey availability for dolphins will be localised.

Further, impacts from interactions with dredging and transport vessels are unlikely to

present a significant risk, as vessel speeds are slow and dolphins do not appear to be

attracted to active dredging operations. The OA community identified in this study

would be the relevant local population for any EIA of any future proposal to expand

the current shellsand dredging operation.

3.5.2.3. Desalination in Gage Roads (GR)

Impact assessments for the operation of desalination plants in southern Australia

have reported low risks of impacts for marine mammals (e.g., Wonthagii, Victoria,

Minister for Minister for Planning 2009) (e.g., Cape Riche or Binningup, southern

Western Australia, Water Water Corporation 2008; Bejder 2011). However, direct

and indirect impacts from brine discharges to the benthic environment (and

subsequently to local fauna populations) remain unknown in these areas (Bejder

2011). In Binningup, for example, prey availability may have been reduced

indirectly from osmoregulation impacts to fish (e.g., cuttlefish, Sepia apama,

Dupavillon and Gillanders 2009; Smith and Sprogis 2016) or destruction of fish

habitats. Such impacts have been reported elsewhere. In Alicante Bay in the

northwestern Mediterranean Sea, for example, a seagrass die-off resulted from

physiological stress caused by salinity fluctuations associated with brine discharge

from two desalination plants (Garrote-Moreno et al. 2014). Physiologically, dolphins

and other marine mammals are highly-evolved osmoregulators, with a kidney

structure developed for habitats with a broad salinity range, indicating that higher


92

localised salinities should not cause significant physiological stress if exposure to

extreme conditions is not prolonged (Ortiz 2001).

The ecological characteristics of the GR community, principally low site fidelity and

more transient behaviour, suggest the operation of a proposed desalination plant in

the northern metropolitan waters of Perth (as has been discussed -- see Mercer

(2013)) would be unlikely to have as adverse an impact as might occur for a

community showing strong site fidelity and near continuous occupancy of the

affected area. Nonetheless, environmental change may induce displacement (Dungan

et al. 2012) or splitting (Nishita et al. 2015) of the community, which may have

adverse ecological impacts because some individuals are essential for maintaining

the cohesion of the network (i.e., metapopulation) and controlling the flow of

information within it (Lusseau and Newman 2004).

3.5.2.4. Harbour construction in Cockburn Sound (CS)

Two harbour developments have been proposed for the Kwinana Shelf region in CS,

a private port and a new Outer Harbour facility for the Port of Fremantle. The

construction of harbour facilities presents a range of risks for dolphins, including

reduction or displacement of dolphins because of direct and indirect impacts from

construction-related activities (e.g., Pirotta et al. 2013; Todd et al. 2015; Culloch et

al. 2016). Potential impacts on dolphins include but are not limited to: (1)

disturbances or changes in behaviour from construction noise and vibration; (2)

displacement due to a change in prey availability resulting from modification or

removal of habitat because of dredging or the construction of infrastructure; and (3)

health issues arising from changes in water quality (e.g., sedimentation and increased

incidence of algal blooms), circulation patterns (e.g., reduced flushing), and

increased chemical contaminants (Environmental Protection Authority 1998).

Here, the risks of a harbour development are significant because the core area of the

CS community is located in the Kwinana Shelf, an area that has ecological

significance for dolphins as foraging habitat and as a nursery area (Finn 2005; Finn

and Calver 2008). In particular, the loss of nursing habitat for females and calves

may impose substantial fitness costs on individual dolphins through reduced


93

reproductive success. The strong, long-term spatial association of dolphins with the

area and the absence of habitat with environmental characteristics similar to the

Kwinana Shelf suggest that dolphins would not be able to compensate for the loss of

habitat on the Kwinana Shelf by shifting to other, nearby areas. The extent to which

dolphins use new harbour facilities for foraging may depend on the harbour designs,

the materials used, and the fish assemblages those areas ultimately sustain.

3.5.3. Conclusions

This study emphasises the need for EIAs to focus on the relevant local wildlife

populations that will be affected by proposed developments and activities. Here, I

applied a methodology of broad utility to identify multiple communities of

bottlenose dolphins in a heterogeneous coastal environment and then used

information on their social and spatial structures, residency patterns, and abundances

used to assess the vulnerability of each community to a particular environmental

impact. Such results could also be informative for Marine Spatial Planning and

Cumulative Impact Mapping. One local population, the SCR community, appeared

to be at some risk of local extinction because of its small size and reliance on an

estuary which is only connected to adjacent coastal waters by a heavily-used harbour

area.

While mobile marine fauna such as bottlenose dolphins may range over large areas

of ocean or coastline, they may also exhibit fine-scale population structure reflecting

long-term residency, strong site fidelity, limited ranging patterns and strong, long-

term associations with particular conspecifics. Other species may exhibit short-term

residency in defined coastal areas, e.g., for breeding or feeding. The proper

evaluation of impacts of coastal and estuarine developments therefore requires

information about the distribution of species at an individual level (i.e., spatial and

temporal scales) and their connection at community level (i.e., metapopulation

dynamics).


94

Appendices

Appendix A3.1. Social dendrogram

Figure A3.1.1. Dendrogram showing average linkage cluster analysis of bottlenose

dolphins sighting at least five times in the Perth metropolitan waters (n = 129

individuals, excluding calves). The HWI of 0.022 indicated the best cut-off value for

forming four groupings based on the maximum modularity (M = 0.526).


95

Appendix A3.2. Tests of differences in distribution of centrality values between

communities

Table A3.2.1. P-value of the Mann-Whitney tests showing the significant

differences in distribution of centrality values (strength, clustering coefficient and

affinity) between communities (mean metric values were calculated for individuals

sighted at least five times).

ComA

vs.

ComB

ComB

vs.

ComC

ComB

vs.

ComD

ComA

vs.

ComC

ComA

vs.

ComD

ComC

vs.

ComD

Strength < 0.01 < 0.001 < 0.001 < 0.001 < 0.001 NS

Clustering

coefficient < 0.001 < 0.001 < 0.001 NS NS < 0.001

Affinity < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 NS

Note: NS = non-significant (P-value > 0.05).


96

Appendix A3.3. Network parameters

Table A3.3.1. Average strength, clustering coefficients and affinity (SE) with

comparisons from random calculating using half-weight indices (including

individuals sighted less than five times).

Strength Clustering Affinity

coefficient

ComA

Mean (n = 117) 11.92 (7.96)* 0.46 (0.20)* 13.23 (6.42)

Random 11.88 (7.98) 0.44 (0.20) 13.46 (6.20)

ComB

Mean (n = 27) 3.72 (1.50)* 0.24 (0.03) 4.30 (0.28)

Random 3.70 (1.52) 0.28 (0.04) 4.32 (0.20)

ComC

Mean (n = 68) 6.37 (3.85)* 0.27 (0.18) 7.74 (2.18)

Random 6.36 (3.89) 0.26 (0.18) 8.10 (1.93)

ComD

Mean (n = 96) 6.65 (3.83)* 0.22 (0.10)* 8.13 (2.05)

Random 6.62 (3.89) 0.19 (0.10) 8.32 (1.83)

Note: n = number of samples; Significant differences from 1000 random networks:

P-value < 0.05.


97

Appendix A3.4. Best-fitted models for the lagged association rate (LAR)

Table A3.4.1. Best-fitted models for the LAR of each network cluster (ComA, ComB, ComC and ComD), regardless of the sex class.

Community Best-fitted models QAIC ΔQAIC Pref. comp % Casual % Time casual

ComA

(n = 15)

Rapid dis. + pref. comps 538.7228 0 36.41%

(SE 0.06)

Pref. comps + casual acqs 540.7228 2 36.41%

(SE 0.06) 63.59%

0.07days

(SE 0.09 days)

ComB

(n = 25)


(SE 0.04) 63.50%

2.84 days

(SE 2.69 days)

Rapid dis. + pref. comps + casual acqs 7386.1142 0.5167 36.42%

(SE 0.04)

21.34%

(SE 1126278.48)

7.07 days

(SE 0.21 days)

ComC

(n = 36) Rapid dis. + Pref. comps 1451.1593 0

26.91%

(SE 0.05)

ComD

(n = 53)


(SE 0.03)

Rapid dis. + casual acqs 2373.3551 1.1879 20.26%

(SE 0.04)

9696.50 days

(SE 4,279.91 days)

Pref. comps + casual acqs 2373.9162 1.7490 19.20%

(SE 0.03) 80.80%

0.03 days

(SE 0.04 days)

Note: n = number of samples; Rapid dis. = Rapid dissociation; Pref. comps = Preferred companions; Casual acqs = casual acquaintances.


98

100

Table A3.4.2. Best-fitted models for the LAR of females for each network cluster (ComA, ComB, ComC and ComD).


ComB

(n = 13) Rapid dis. + pref. comps + casual acqs 2897.1046 0

31.40%

(SE 0.05)

16.49%

(SE 91324806)

33.32 days

(SE 0.15 days)

ComC

(n = 14)

Rapid dis. + pref. comps 810.6382 0 24.84 %

(SE 0.05)

Pref. comps + casual acqs 812.6381 1.9999 21.84%

(SE 0.05) 78.16%

0.04 days

(SE 0.03 days)

ComD

(n = 25)

Rapid dis. + Pref. comps 557.2738 0 18.53%

(SE 0.04)


(SE 0.04) 81.48 %

0.16 days

(SE 0.49 days)



99

Table A3.4.3. Best-fitted models for the LAR of males for each network cluster (ComA, ComB, ComC and ComD).


ComB

(n = 12)


(SE 0.04) 41.66%

3.64 days

(SE 0.12 days)

Rapid dis. + pref. comps 4167.2745 0.9024 58.58%

(SE 0.04)

Rapid dis. + pref. comps + casual acqs 4168.3165 1.9444 58.34%

(SE 0.04)

33.28%

(SE 1613350)

4.19 days

(SE 0.20 days)

ComC

(n =11)


(SE 0.06)

Rapid dis. + casual acqs 254.5568 1.4398 31.79%

(SE 0.15)

3625.55 days

(SE 1498.51 days)

Pref. comps + casual acqs 255.1170 2 27.83 %

(SE 0.06) 72.17%

0.07 days

(SE 1.05 days)

ComD

(n = 19)


(SE 0.06)


(SE 0.06) 69.73%

0.04 days

(SE 0.04 days)



100

Appendix A3.5. Core areas and home ranges

Table A3.5.1. Core area (50% kernel density, km2) and home range (95% kernel

density, km2) of each community using all individuals sighted or only individuals

sighted more than five times (restricted).

Core area Home range

All Restricted

All Restricted

ComA (GR) 19.23 23.67

167.11 172.93

ComB (SCR) 6.83 6.91

66.67 66.65

ComC (OA) 31.05 27.9

137.5 131.04

ComD (CS) 24.35 23.75 125.79 123.48




101

Appendix 3.6. Bathymetry

Table A3.6.1. Depth coverage (%) of the restricted core areas (50% kernel density)

and home ranges (95% kernel density, km2) of each community. Depth classes are:

shallower than 5 m; between 5 and 10 m; between 10 and 15 m; and deeper than 15

m.

Core area Home range

< 5 5-10 10-15 > 15 < 5 5-10 10-15 > 15

ComA (GR) 40.8 42.1 13.2 3.9 16.4 28.2 31.0 24.4

ComB (SCR) 26.8 2.4 41.5 29.3 26.8 34.6 22.1 16.5

ComC (OA) 19.0 35.0 38.0 8.0 14.4 24.1 24.3 37.2

ComD (CS) 7.4 18.5 33.4 40.7 14.4 20.8 26.8 38.0




102

Appendix A3.7. Overlap between social communities

Table A3.7.1. Area (km2) of overlap between the four social communities.

From

Into GR SCR OA CS

ComA (GR) -

55.55 7.96

ComB (SCR) 18.83 - 33.90 14.49

ComC (OA) 33.92 39.93 - 45.62

ComD (CS) 2.42 12.79 43.97 -




10

3

Appendix A3.8. Best-fitted models for the lagged identification rate (LIR)

Table A3.8.1. Best-fitted models for the LIR for each network cluster (ComA, ComB, ComC and ComD). The 95% confidence intervals are

given in italic.

Community Best-fitted models QAIC ΔQAIC Community size

(N)

Mean time in

(days)

Mean time out

(days)

Mortality

ComA

(n = 117)

Emigration + mortality 2731.0889 0 68

60-85

2061.88

1253.64-3971.52

Emigration + reimmigration

+ mortality 2731.8408 0.6274

23

8-39

9.99

4.63-18.18

21.17

10.62-41.91

0.0004

0.0001-0.0007

ComB

(n = 27) Emigration + mortality 34521.2798 0

21

18-24

6773.32

3685.52-31792.77

ComC


43

36-50

2743.37

1504.16-8333.60

ComD


64

56-73

4485.11

2424.79-17045.43

Note: n = number of individuals.

105

Chapter 4. Genetic structure of socially and spatially discrete

subpopulations of dolphins (Tursiops aduncus) in Perth, Western

Australia

4.1. Abstract

Populations of Indo-Pacific bottlenose dolphins (Tursiops aduncus) in Perth

metropolitan waters (Western Australia) are spatially segregated into four discrete

social subpopulations, although the level of genetic differentiation within the

population is unknown. Contemporary and historical genetic differences among the

four socio-geographic subpopulations were assessed using ten microsatellite loci and

mitochondrial DNA control region sequence data. Pairwise estimates of genetic

differentiation (FST) based on microsatellite alleles revealed some significant

differences between the socio-geographic subpopulations, also supported by little or

no contemporary gene flow. However, differences were too weak and recent to

assign individuals to their original subpopulations using the Bayesian clustering

implemented in STRUCTURE. Historically, individuals from the socio-geographic

subpopulations originated from two ancestral lineages, although no apparent

geographic clustering was detected. The Tajima’s D analysis suggested the

occurrence of a historical bottleneck event or selection in at least one socio-

geographic subpopulation (Cockburn Sound) associated with a semi-enclosed

embayment, although none of the other bottleneck tests agreed. Likewise, high

genetic diversity was maintained by moderate asymmetric gene flow (m > 0.10) and

could have erased any bottleneck or selection signal. Within this source-sink

dynamic, it is important to conserve the source subpopulation as other

subpopulations depend on it. The estuarine subpopulation, which acts as a sink, also

warrants particular attention for conservation and management because of potential

inbreeding and vulnerability to extinction because of its small population size and

impacts from multiple anthropogenic threats.

Chapter 4 – Genetic structure of socio-geographic subpopulations

106

4.2. Introduction

Understanding population structure is a challenging yet vital component of wildlife

conservation, particularly for continuously distributed marine taxa like cetaceans

(Baker et al. 1998; Waples 1998; Thompson et al. 2016). Low levels of population

genetic structure are generally expected for cetaceans because of the general lack of

geographical barriers to gene flow in marine environments and the high dispersal

capacity of cetaceans (Fontaine et al. 2007; Thompson et al. 2016). Nonetheless,

clear genetically discrete population units often occur for many cetacean species,

even at fine geographical scales (Hoelzel et al. 1998b; Sellas et al. 2005; Andrews et

al. 2010; Fernández et al. 2011; Möller et al. 2011; Ansmann et al. 2012; Brown et

al. 2014). Genetic differentiation between populations may be a result of isolation by

distance or be related to physical features or geographic separation (e.g., Krützen et

al. 2004). However, it can also be attributable to complex behavioural factors

associated with ecological and environmental processes, such as local resource

specialisations, philopatry or social organisation (Gaggiotti et al. 2004; Möller et al.

2007; Andrews et al. 2010; Wiszniewski et al. 2010; Ansmann et al. 2012; Kopps et

al. 2014).

For bottlenose dolphins (Tursiops spp.), the geographical scale at which genetic

structure appears is highly dependent on the nature of the surrounding environment

(Wiszniewski et al. 2010). Little differentiation has been observed (with both nuclear

and mitochondrial DNA markers) in large pelagic populations (e.g., Quérouil et al.

2007). By contrast, clear genetic differentiation may occur within populations

occupying coastal habitats, despite some reproductive exchange (Sellas et al. 2005;

Möller et al. 2007; Rosel et al. 2009; Tezanos-Pinto et al. 2009; Urian et al. 2009;

Mirimin et al. 2011). Such differentiation is often related to the coastal and estuarine

habitats present, e.g., open coastlines, embayments, lagoons, sounds, tidal marshes

and river systems (Sellas et al. 2005; Richards et al. 2013). Where a number of

coastal and estuarine habitats are present, and environmental heterogeneity is high,

the possibility for fine-scale population subdivision increases (Krützen et al. 2004;

Wiszniewski et al. 2010; Ansmann et al. 2012).


107

Strong site fidelity, with resident subpopulations inhabiting estuaries and bays as a

consequence of different social systems and behavioural strategies, is a factor that

may lead to genetic structure (Hoelzel et al. 1998b; Parsons et al. 2006; Wiszniewski

et al. 2009). Social structure can play a critical role in shaping genetic variability

within and between populations since it can determine patterns between related and

unrelated individuals in space and time (Sugg et al. 1996; Storz 1999). High

philopatry and long-term social affiliations between females may result in non-

random mating and reduced gene flow among inshore subpopulations, as will a

moderate level of male philopatry (Connor et al. 2000a; Krützen et al. 2004; Möller

and Beheregaray 2004; Möller et al. 2006).

Dedicated line-transect photo-identification surveys revealed that the population of

Indo-Pacific bottlenose dolphins (Tursiops aduncus) inhabiting the coastal and

estuarine waters of Perth (Western Australia) consists of small subpopulations that

are geographically, socially and demographically differentiated (see Chapters 2 and

3, Chabanne et al. 2017a, b). Three socially stable subpopulations exhibit long-term

residency in different habitats, namely an estuary and an embayment. A fourth one,

along an open coastline, was described as a transient subpopulation with

characteristics suggesting that only a small portion of a larger population located

outside the boundaries of the study area has been identified. Despite strong social

and spatial segregation between subpopulations, sightings of mixed groups also

occurred, suggesting the possibility for interbreeding between subpopulations which

would minimise local genetic differentiation. Nonetheless, while an interbreeding

scenario may be the most plausible, Sellas et al. (2005), for example, found

significant genetic differentiation between Sarasota Bay resident dolphins and

nearshore coastal Gulf of Mexico dolphins just outside the Bay with only a small

amount of interbreeding, despite sightings of mixed groups.

Understanding whether bottlenose dolphins in the Perth metropolitan area represent

several distinct subpopulations that reflect their social and spatial segregation, or a

single genetic population, will play a major role in defining management

requirements and planning for the conservation of the species in this region. Findings

will be extremely valuable since an unusual mortality event occurred in the Swan

Canning Riverpark in 2009, with the death of six bottlenose dolphins within a six-


108

month period (Holyoake et al. 2010). To this end, I investigated the genetic

differentiation and diversity among and within the identified socio-geographic

subpopulations (see Chapters 2 and 3, Chabanne et al. 2017a, b) using microsatellite

DNA markers and a 416-base pair (bp) fragment of the mitochondrial (mt) DNA

control region. Understanding the genetic structure of this population will

complement the social and spatial segregation findings and aid in making

appropriate decisions for management and enhance conservation of the local

subpopulations that are highly impacted by human activities.


4.3.1. Genetic sample collection

Systematic boat-based surveys for Indo-Pacific bottlenose dolphins in Perth

metropolitan waters, Western Australia, (Figure 4.1) were carried out from June

2011 to May 2015 following a mark-recapture design approach (see Chapter 2,

Chabanne et al. 2017b). Analyses of the social structure based on those surveys and

photo-identification defined a population subdivided into four socio-ecological

subpopulations (see Chapter 3, Chabanne et al. 2017a). Spatial segregation was also

described with each subpopulation inhabiting a particular geographic region in the

study area: GR – Gage Roads, which represents an open coastline; SCR – Swan

Canning Riverpark, which is an enclosed estuary; OA – Owen Anchorage, which is

the northern section of a large semi-enclosed embayment that is mainly shallow; and

CS – Cockburn Sound, which is the southern section of the large semi-enclosed

embayment with deeper waters.


109

Figure 4.1. Map of the four geographic regions (in bold) associated with each socio-

ecological subpopulation within the Perth metropolitan waters, Western Australia.

During systematic surveys and other opportunistic surveys, biopsy samples were

collected using the PAXARMS remote biopsy system specifically designed for small

cetaceans (Krützen et al. 2002). A few individuals observed between 2011 and 2015

were biopsied during opportunistic surveys conducted between 2007 and 2011.

Calves assumed to be less than two-year-old (i.e., based on body length and

approximate date of birth) were excluded from biopsy sampling. Tissue samples

were preserved in saturated NaCl 20% dimethyl sulfoxide until DNA extraction.


110

4.3.2. DNA extraction and sexing

Genomic DNA was extracted from all skin samples using the Gentra Puregene

Tissue Kit (Qiagen) and following the manufacturer’s protocol. The sex of sampled

individuals was determined using fragments of the ZFX and SRY genes (Gilson et

al. 1998) that were amplified using polymerase chain reaction (PCR) with 20-25 ng

DNA, 0.15 μl of each primer (ZFX forward and reverse and SRY forward and

reverse) and standard PCR reagents. The PCR profile consisted of initial

denaturation at 95°C for 4 min, followed by 35 cycles of 94°C for 45 s, 50°C for 45 s

and 72°C for 10 min. PCR products were separated on agarose gel to determine sex

base on length differences.

4.3.3. Genotyping and validation of microsatellites

Four primers were used to optimize a total of 13 microsatellite loci: DIrFCB4,

DIrFCB5 (Buchanan et al. 1996), LobsDi_7.1, LobsDi_9, LobsDi_19, LobsDi_21,

LobsDi_24, LobsDi_39 (Cassens et al. 2005), SCA9, SCA17, SCA22, SCA27 (Chen

and Yang 2008), TexVet5, TexVet7 (Rooney et al. 1999). I followed the PCR

conditions as described in Frère et al. (2010a). Single stranded PCR products were

run on an ABI 3730 DNA Sequencer (Applied Biosystems). Using GENEIOUS 9.1

(http://www.geneious.com; Kearse et al. 2012) with the microsatellite plugin 1.4

(Applied Biosystems), bins for each locus were determined, and genotypes scored.

Each microsatellite locus was checked for scoring errors using the software Micro-

Checker 2.2 with a confidence level of 95% (Van Oosterhout et al. 2004). Samples

that matched in sex and microsatellite genotypes were considered duplicates, and

only one of each was retained for analyses. Departures from Hardy-Weinberg

equilibrium (HWE) and linkage disequilibrium were tested using the Markov chain

probability test and 104 iterations in Genepop 4.4 (Rousset 2008). Significance

values for multiple comparisons were adjusted by sequential Bonferroni corrections

(Rice 1989). I used the software INEST 2.0 (Inbreeding/Null Allele Estimation;

Chybicki and Burczyk 2009) to check whether any departure from HWE at a given

locus might be explained by the presence of null alleles or by inbreeding and

estimated their frequencies using the population inbreeding model. The probability

of identity (PI) was calculated using GenAlEx 6.3 (Peakall and Smouse 2006) to


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assess the discriminatory power of the set of microsatellite loci and calculate the

probability that two different individuals could share the same genotype.

4.3.4. Mitochondrial (mt) DNA sequencing

Primers dlp1.5 (5′-TCA CCC AAA GCT GRA RTT CTA-3′) and dlp5 (5′-CCA

TCG WGA TGT CTT ATT TAA GRG GAA-3′) (Baker et al. 1993) were used to

amplify a 412-bp mitochondrial fragment following PCR conditions described in

Bacher et al. (2010). Sequences of the mtDNA were manually edited using the

program GENEIOUS 9.1.

4.3.5. Assessment of genetic differentiation

Sampled individuals were assigned into their respective socio-geographic

subpopulation defined in Chapter 3 (Chabanne et al. 2017a). Genetic differentiation

between socio-geographic subpopulations was investigated by calculating pairwise

genetic distances FST between microsatellite alleles and between mtDNA haplotypes

(Weir and Cockerham 1984). With mtDNA data, I also estimated the nucleotide

differentiation φST (Tamara and Nei, 1993) using the program Arlequin 3.5

(Excoffier and Lischer 2010). Significance levels were tested using 104 permutations

(AMOVA). Significance values for multiple comparisons were adjusted by

sequential Bonferroni corrections (Rice 1989). For both marker sets, I also estimated

pairwise Shannon’s Mutual Information index, sHUA using GenAlEx 6.3 (Sherwin et

al. 2006) for which tests of significance are performed by random permutations (n =

999 permutations) rather than by the more conservative chi-square test.

4.3.6. Assessment of genetic population structure

To verify the hypothesis that social structure reflects the genetic structure, I used the

software STRUCTURE 2.3 (Pritchard et al. 2000) to determine the most likely number

of distinct nuclear genetic clusters (K) and compare the genetic cluster assignment

with the social assignment of each individual. The software uses a Markov chain

Monte Carlo (MCMC) procedure to estimate the mean log probability (LnP(D)) that


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the data fit the hypothesis of K clusters. I ran the analysis using an admixture model

(with or without prior information on social subpopulation), and all models were run

with correlated allele frequencies (recommended by Falush et al. 2003); a burn-in

period set to 105 following by 10

7 MCMC steps and was independently run ten times

for each K cluster. The likely number of clusters (K) was set to values from 1 to 6.

As the LnP(D) estimator has been shown to overestimate K, as it frequently plateaus

at higher values than biologically meaningful estimates of K, I also calculated the Δk

statistic (Evanno et al. 2005).

4.3.7. Analysis of gene flow

Contemporary migration rates among the bottlenose dolphin social subpopulations

were estimated using the Bayesian multilocus genotyping approach implemented in

the program BayesAss 3.0 (Wilson and Rannala 2003). I reached the acceptance

rates of total iterations (i.e., between 20 and 60%) by adjusting the parameters

migration rates (m), allele frequencies (a) and inbreeding coefficient (f) to 0.3, 0.5

and 0.6, respectively. Five independent runs were performed using 107

Markov chain

Monte Carlo (MCMC) iterations, 106 burn-in and sampled every 10

3 iterations.

Convergence was examined using the software Tracer 1.6 (Rambaut et al. 2013). In

addition, sex-biased dispersal was analysed in GenAlEx (Peakall and Smouse 2012),

by calculating sex-specific assignment index correction (AIc) and testing difference

for statistical significance using the Mann-Whitney U test (Mossman and Waser

1999).

4.3.8. Assessment of genetic diversity

For microsatellites, I assessed the genetic diversity within social subpopulations by

calculating the number of alleles (NA), private alleles† (NPA), and observed (HO) and

expected (HE) heterozygosities in GenAlEx (Peakall and Smouse 2012). I also

estimated the allelic richness* (AR) and inbreeding coefficient (FIS) using FSTAT

(Goudet 2001). For mtDNA, identification of the haplotypes, and assessment of

haplotypic diversity† (h) and nucleotide diversity

† (π) were performed in DnaSP 5.10

(Librado and Rozas 2009). I constructed a haplotype network using median-joining


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implemented in NETWORK 5.0 (Bandelt et al. 1999) to assess genealogical

relationship.

Both microsatellites and mtDNA were tested for evidence of recent bottleneck†

events. For microsatellites, I used the software BOTTLENECK 1.2 (Cornuet and

Luikart 1996) and assessed the significance of Wilcoxon sign rank tests for two

models of generation of new alleles: the stepwise mutation model (SMM) and the

two-phased model of mutation (TPM; variance = 30, 70% stepwise mutational

model, 103 iterations). I also inspected the distribution of allelic frequencies to detect

a mode-shift distortion due to the loss of rare alleles (Luikart et al. 1998). For

mtDNA, signals of population size reduction (based on the assumption of neutrality)

were assessed using Tajima’s D test (Tajima 1989) and Fu’s FS test (Fu 1997)

implemented in Arlequin. P-values of both statistics were generated using 103

simulations

4.4. Results

4.4.1. Validation of genotypes and haplotypes

A total of 155 biopsy samples were collected from bottlenose dolphins from 2007 to

2015. Thirteen samples were identified as duplicates through confirmation by both

identical microsatellite genotypes and photographic identification. One sample was

confirmed to be the same individual via photography but not all loci alleles matched,

suggesting an issue in the alignment of the DNA. Another 32 samples were of

individuals never seen during the systematic surveys conducted from June 2011 to

May 2015 (see Chapters 2 and 3, Chabanne et al. 2017a, b). As I aimed to verify

whether or not the social structure previously described among bottlenose dolphins

in the study area (see Chapter 3, Chabanne et al. 2017a) was influenced by genetic

differentiation, those samples were removed as well as the duplicated ones from the

data set, leaving a total of 109 different sampled individuals.

Micro-Checker analysis of microsatellite data did find evidence of scoring errors due

to stuttering or large allele dropout at one locus (SCA27). Test for HWE at the

population level indicated significant departure for loci TexVet5, and SCA27, both


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due to homozygosity excess that could be explained by the presence of null alleles

(Table 4.1). Using INEST program, frequencies of null alleles with consideration of

inbreeding for loci TexVet5 and SCA27 were of 0.0978 (SE 0.0010) and 0.1034 (SE

0.0012), respectively. Substantial missing data existed at SCA17 due to allele

scoring difficulties. Therefore, I excluded TexVet5, SCA27 and SCA17 from further

analyses. After sequential Bonferroni correction, there was no significant linkage

disequilibrium between any pair of loci (see Appendix A4.1).

Table 4.1. Microsatellite diversity in Indo-Pacific bottlenose dolphins inhabiting

Perth metropolitan waters.

Locus n NA HO HE FIS r

DIrFCB4 108 14 0.796 0.833 0.044

0.0196 (0.0005)

LobsDi_19 109 12 0.743 0.746 0.003

0.0123 (0.0005)

LobsDi_21 109 7 0.761 0.729 -0.045

0.0072 (0.0004)

LobsDi_24 109 14 0.798 0.848 0.059

0.0121 (0.0005)

LobsDi_9 105 6 0.657 0.720 0.088 0.0420 (0.0012)

TexVet5 107 9 0.589 0.725 0.188 * 0.0978 (0.0010)

DIrFCB5 109 8 0.633 0.676 0.064

0.0117 (0.0005)

LobsDi_39 108 11 0.787 0.771 -0.021

0.0060 (0.0003)

SCA22 105 18 0.924 0.895 -0.032

0.0049 (0.0002)

SCA27 108 8 0.241 0.324 0.256 * 0.1034 (0.0012)

SCA9 103 17 0.845 0.859 -0.017

0.0063 (0.0003)

TexVet7 108 5 0.472 0.467 -0.012 0.0154 (0.0007)

Note: n = number of screened samples; NA = number of found alleles; HO = observed

heterozygosity; HE = expected heterozygosity; FIS = coefficient of inbreeding; r (SE)

= frequency of null alleles. * Significant departure from Hardy-Weinberg

equilibrium after Bonferroni correction.

All the remaining ten loci were polymorphic with the number of alleles per locus

ranging from five to 18. The probability of two unrelated dolphins sharing the same

genotype (PI) across all ten loci was very low (max PI = 1.1 × 10-11

), indicating a

high discriminatory power of the set of microsatellite loci.

Mitochondrial control region sequences of 412-base pairs were aligned for 99 of the

109 samples and defined six unique haplotypes (see section 4.4.6 below).


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4.4.2. Genetic differentiation

A weak but significant degree of overall genetic differentiation was seen for both

marker data sets. The overall average FST for the ten microsatellite loci was 0.020

(P-value < 0.0005). For the mtDNA data set, the overall FST and ΦST values were

0.127 (P-value < 0.006) and 0.128 (P-value < 0.005), respectively.

Pairwise FST comparisons (Table 4.2) based on microsatellite data again revealed

significant and low genetic differentiation (FST < 0.03), except between GR and SCR

and between GR and OA. MtDNA data, however, returned a significant genetic

differentiation only between OA and CS (ΦST = 0.310, P-value < 0.01, Table 4.2).

Table 4.2. Pairwise fixation indices between four genetic clusters previously defined

in STRUCTURE analysis based on ten microsatellite loci and mtDNA control

sequences. Microsatellite FST values are above the diagonal; Mitochondrial ΦST

values are below. Mitochondrial FST values were similar to ΦST, and thus are not

shown here; values that are significant after sequential Bonferroni correction are in

bold. Values in italic were significant before correction (P-value < 0.05).

Subpopulation GR SCR OA CS

GR - 0.016

0.006 0.018 *

SCR -0.063

- 0.023 ** 0.025 ***

OA -0.037

0.017

-

0.020 **

CS 0.186 0.122

0.310 ** -

Note: * P-value < 0.05, ** P-value < 0.01, *** P-value < 0.001 after sequential

Bonferroni correction. GR = Gage Roads, SCR = Swan Canning Riverpark, OA =

Owen Anchorage, CS = Cockburn Sound.


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Shannon’s mutual information index (SHUA) (Table 4.3) supported some FST and ΦST

results. GR showed no significant differences in allele or mtDNA SHUA to any other

subpopulation after sequential Bonferroni correction, although significant difference

was detected with SCR before correction (P-value < 0.05). All other subpopulations

were significantly different to one another.

Table 4.3. Pairwise comparisons of Shannon mutual information index (SHUA)

among socio-geographic subpopulations based on ten microsatellite loci (allele

frequency differences, above diagonal) and mtDNA control region sequences

(haplotype frequency differences, below diagonal). Significant P-values after

sequential Bonferroni correction and based on statistical testing of 999 random

permutations are shown in bold. Values in italic were significant before correction

(P-value < 0.05).

Subpopulation GR SCR OA CS

GR - 0.152

0.140 0.099

SCR 0.159

- 0.165 ** 0.152 **

OA 0.160

0.221 * -

0.119 **

CS 0.102 0.204 ** 0.137 ** -

Note: * P-value < 0.05; ** P-value <0.01; *** P-value < 0.001 after sequential

Bonferroni correction. GR = Gage Roads, SCR = Swan Canning Riverpark, OA =

Owen Anchorage, CS = Cockburn Sound.

4.4.3. Genetic population structure

Microsatellite data analysis using STRUCTURE with no prior information showed the

highest mean posterior probability (LnP(D)) reached at K = 4 (see Appendix A4.2).

However, the ΔK index obtained by the method of Evanno et al. (2005) revealed a

modal value of ΔK = 20.2 at K = 3 (see Appendix A4.2), although there was no clear

correlation between the genetic clusters and the socio-geographic subpopulations

(Figure 4.2a). When prior information on socio-geographic subpopulations was

included, the LnP(D) mean was higher at K = 1, although not different at K = 2, 3 or

4, and the modal value of ΔK = 4.05 was found at K = 4 (Appendix A4.2).

Individuals assigned to genetic cluster 2 (red) were essentially from the SCR

subpopulation, although their mean proportion of membership q was moderate (q =

0.635) (Figure 4.2b). Similarly, all individuals from the GR subpopulation and

majority of the individuals from the OA subpopulation (n = 86%) were assigned to

genetic cluster 1 (blue) with moderate confidence (q = 0.613 and 0.667,


117

respectively), while individuals from the CS subpopulation were mainly of mixed

ancestry from genetic clusters 1 (blue), 3 (green) and 4 (purple).

Figure 4.2. Bayesian assignment probabilities from STRUCTURE for bottlenose

dolphins based on ten microsatellite loci: (a) without prior information, K = 3. (b)

With prior information, K = 4. Each vertical line represents one individual, with the

strength of that individual to any of the genetic clusters (blue cluster 1; red cluster 2;

green cluster 3; purple cluster 4). Individuals are grouped by social subpopulations

(GR = Gage Roads, SCR = Swan Canning Riverpark, OA = Owen Anchorage, CS =

Cockburn Sound) and sorted within subpopulations by the latitude of sampling

location from North (left) to South (right) when available.

As suggested by Pritchard et al. (2000), in order to further assess possible population

structure within the three coastal subpopulations (GR, OA, and CS subpopulations),

I estimated the number of populations (K) by considering only the individuals from

those subpopulations. No population structure was detected (K = 1, results not

presented).


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4.4.4. Gene flow and dispersal

Estimated contemporary migration rates inferred in BayesAss suggested very low

gene flow between the majority of the social subpopulations (m = 1-4%, Table 4.4).

However, estimated migration rates from OA to other subpopulations were moderate

(m = 23% to 28%), suggesting that OA may act as a source with negligible migration

in the opposite direction. The proportion of non-immigrants from their respective

origin subpopulation was high for OA subpopulation (0.91), while others show lower

proportions (from 0.68 to 0.74).

Table 4.4. Mean (standard deviation) of the posterior distribution of the

contemporary migration rates (m) in BayesAss (Wilson and Rannala 2003) among

four bottlenose dolphin socio-geographic subpopulations in Perth metropolitan

waters. The subpopulations of which each dolphin belongs are listed in the rows,

while the subpopulations from which they migrated are listed in the columns. Values

along the diagonal (in bold) are the proportions of non-immigrants from the origin

subpopulation for each generation. Moderate estimated migration rates (m > 0.10)

are displayed in italic.

Migration Origin:

into: GR SCR OA CS

GR 0.69 (0.02) 0.03 (0.02) 0.26 (0.03) 0.02 (0.02)

SCR 0.01 (0.01) 0.74 (0.03) 0.23 (0.03) 0.02 (0.01)

OA 0.03 (0.02) 0.04 (0.02) 0.91 (0.03) 0.02 (0.02)

CS 0.02 (0.01) 0.01 (0.01) 0.28 (0.02) 0.68 (0.01)



4.4.5. Sex-biased dispersal

Of the total of 109 dolphins sampled, 56 were females, and 53 were males. Analysis

of mtDNA sequences among subpopulations did not reveal significant structuring for

females and males (Table 4.5) and the corrected mean assignment indices for

females and males did not reveal any sex-biased dispersal (Z = -0.177, P-value =

0.860) (see Appendix A4.3). However, because the differentiation FST is a function

of 1/(1+4Nm) (Wright 1931), and mtDNA effective population size Nmt should

represent 0.25 of the microsatellite effective population size Ne (Avise et al. 1987),

my results suggested restricted female movement with a much lower Nm for mtDNA


119

than expected if both males and females have similar dispersal (m) (see Appendix

A4.4 for details). However, values of FST for mtDNA should be used with caution

since FSTAT estimated negative values.

Within sex classes, differentiation of nuclear variation, suggested some level of

genetic differentiation among males from SCR and CS and among females from

SCR, OA and CS subpopulations (Table 4.5).

Table 4.5. Sex-specific FST values based on microsatellite loci and mtDNA for all

and for each pairwise socio-geographic subpopulations. Significance levels of

genetic differentiation between socio-geographic subpopulations are also indicated

for FST values (estimated with Arlequin, after Bonferroni correction: * P-value <

0.05).

FST microsatellite FST mtDNA

Females Males Female Male

All 0.019 0.021 0.142 0.114

GR - SCR 0.003 0.022 0.006 -0.066

GR - OA 0.005 0.007 0.126 0.150

GR - CS 0.017 0.018 0.036 -0.086

SCR - OA 0.027 * 0.034 0.309 0.311

SCR - CS 0.022 * 0.026 * -0.119 0.020

OA - CS 0.022 * 0.023 0.502 -0.061

Note: GR = Gage Roads, SCR = Swan Canning-Riverpark, OA = Owen Anchorage,

and CS = Cockburn Sound.

4.4.6. Genetic diversity

Levels of microsatellite diversity were high for all subpopulation samples as

measured by both allelic richness (AR) and expected heterozygosity (HE). Allelic

richness (AR) ranged from 5.9 to 6.6 and expected heterozygosity (HE) from 69% to

76% (Table 4.6). The average number of alleles (NA) per subpopulation ranged from

6.0 to 9.6 with an overall value of 11.2. Out of a total of 29 private alleles identified,

four were found in OA, seven in SCR and 18 in CS. When null alleles were

acknowledged (INEST tests), none of the estimated inbreeding (FIS) values were

significantly different from zero except for SCR.


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Table 4.6. Genetic diversity measures (SE) for bottlenose dolphin socio-geographic subpopulations using microsatellite loci (n = 10) and

mtDNA.

Microsatellites mtDNA

n Nf Nm NA NPA AR HE HO FIS

FSTAT

INEST n NH h π

Overall 109 56 53 11.2

(1.5) -

6.5

(0.7)

0.75

(0.04)

0.74

(0.04)

-0.020 NS

-0.021 NS

99 6

0.585

(0.044)

0.014

(0.002)

GR 14 7 7 6.0

(0.8) 0

5.9

(0.8)

0.69

(0.04)

0.69

(0.05)

-0.004 NS

-0.025 NS

11 4

0.691

(0.128)

0.020

(0.006)

SCR 24 13 12 7.1

(0.9) 7

5.9

(0.7)

0.70

(0.05)

0.68

(0.05)

-0.050 NS

-0.048 * 21 4

0.414

(0.124)

0.017

(0.005)

OA 22 13 9 7.4

(0.8) 4

6.2

(0.6)

0.72

(0.04)

0.74

(0.05)

-0.008 NS

-0.010 NS

21 3

0.667

(0.050)

0.023

(0.003)

CS 48 23 25 9.6

(1.3) 18

6.6

(0.7)

0.76

(0.04)

0.78

(0.04)

-0.014 NS

-0.013 NS

46 4

0.532

(0.053)

0.005

(0.002)

Note: n = number of samples; Nf = number of females; Nm = number of males; NA = mean number of alleles; NPA = number of private alleles;

AR = mean allelic richness; HE = expected heterozygosity; HO = observed heterozygosity; FIS = Inbreeding coefficient calculated both in FSTAT

using the original dataset and in INEST using a null-allele corrected dataset; NH = number of haplotypes; h = Haplotypic diversity; π =

nucleotide diversity. NS = non-significant; * FIS significantly different to zero P-value < 0.05 two-tailed). GR = Gage Roads, SCR = Swan

Canning Riverpark, OA = Owen Anchorage, CS = Cockburn Sound.


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For the mtDNA, a total of 21 polymorphic sites were found among samples, defining

six unique haplotypes. There were two common haplotypes (H1, H2), and a third

haplotype (H3) shared by all except SCR. There was one rare haplotype (H6 found

in three samples from GR and SCR), as well as two unique haplotypes each found in

only one sample (H4 found in CS and H5 found in SCR). As a result, moderate

haplotypic (h) but low nucleotide (π) diversity was observed (h: 0.414-0.683; π:

0.005-0.022) (Table 4.6).

The median-joining network showed two main groups of haplotypes with a central

core showing a minimum of 17 mutational steps (unsampled or extinct haplotypes)

(Figure 4.3). However, there was no clear clustering based on socio-geographic

subpopulations.

Figure 4.3. Median-joining network of mtDNA control region haplotypes in Indo-

Pacific bottlenose dolphins in Perth metropolitan waters. The size of the circles is

proportional to the total number of individuals carrying that haplotype. Different

colours denote the four different sampled subpopulations: purple GR = Gage Roads,

red SCR = Swan Canning Riverpark, green OA = Owen Anchorage and yellow CS =

Cockburn Sound. Number of mutational events between each haplotype is indicated

by hash marks.

For mtDNA, Tajima’s and Fu’s tests for selection or population size change did not

differ significantly from expected under a neutral model of evolution for the overall

and any of the socio-geographic subpopulations, except CS which showed a

significantly negative Tajima’s D value (Table 4.7). Similarly, the two tests for a

bottleneck using allele frequencies of microsatellite loci (SMM and TPM) did not

show significant heterozygosity excess after correcting for multiple comparisons


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(Table 4.7), suggesting no evidence of a recent decline or colonisation event.

Additionally, the distribution of allelic frequencies did not show significant

departure from a standard L-shape in the model-shift test, indicating no loss of rare

alleles in any subpopulations (see Appendix A4.5).

Table 4.7. Summary statistics of various tests to detect a recent bottleneck effect

based on mtDNA control region (Tajima’s D and Fu’s Fs) and microsatellite loci

(SMM: stepwise mutation model. TPM: two-phased model). The Wilcoxon test

found no significant heterozygosity excess after Bonferroni correction (Pcrit = 0.012).

mtDNA Microsatellites

Wilcoxon test (P-value)

Tajima's D Fu's Fs SMM TPM

Overall 1.120 1.730 0.999 0.652

GR 1.154 5.414 0.188 0.839

SCR 0.863 8.432 0.997 0.313

OA 2.936 13.046 0.999 0.652

CS -1.748* 3.325 0.990 0.186

Note: * significant value, P-value < 0.05; GR = Gage Roads, SCR = Swan Canning

Riverpark, OA = Owen Anchorage, CS = Cockburn Sound.

4.5. Discussion

Despite the significant FST and sHUA differentiations found between three out of four

socio-geographic subpopulations defined in Chapter 3 (Chabanne et al. 2017a) -

SCR, OA and CS - the high gene flow from OA supports several factors indicating

the potential for interbreeding (Litz 2007), namely: the high mobility of the species,

the small size of the study area (c. 300 km2), the evidence of home range overlapping

and the occurrence of mixed groups between subpopulations (see Chapter 3,

Chabanne et al. 2017a).

The genetic structure of bottlenose dolphins is typically assessed based on individual

spatial information (e.g., Krützen et al. 2004; Natoli et al. 2004; Charlton-Robb et al.

2014; Fruet et al. 2014; Gaspari et al. 2015; Allen et al. 2016). Although spatial

segregation was found among bottlenose dolphins in Perth (see Chapter 3, Chabanne

et al. 2017a), here I investigated the genetic structure based on the social structure

described in the population of bottlenose dolphins in the study area (see Chapter 3,


123

Chabanne et al. 2017a). The social structure was defined from photo-identification

data collected between 2011 and 2015 and would, therefore, reflect less than a single

generation, defined as the average age of females at the time of the birth of their first

female calf, from demographic data (Manlik et al. 2016). Using microsatellite loci

and mtDNA markers, I was able to obtain information on distinct timescales. As

microsatellite markers are biparentally inherited with relatively fast mutation rates,

they allow insights of recent and almost contemporaneous events (i.e., from the last

few generations). In contrast, mtDNA markers reflect more historical events due to

their relatively slow mutation rates associated with their maternal mode of

inheritance (Avise et al. 1987). Comparing microsatellite loci and mtDNA marker

variation may also permit the detection of differences in migration rates between

sexes, using the assumption that the effective population size Nmt of mtDNA is 0.25

that of microsatellite Ne (Avise et al. 1987).

4.5.1. Genetic structure and gene flow

Pairwise FST comparisons (Table 4.2) of genetic differentiation based on

microsatellite loci found significant contemporary differences between SCR, OA,

and CS as well as between GR and CS. However, values of FST were weak with a

maximum value of 0.025 between SCR and CS. The high variation in microsatellites

for all subpopulations (Table 4.6) may provide high statistical power and thus

increase the ability to pick up small differences in allele frequencies that are not

biologically meaningful (Hedrick 1999; Kalinowski 2002). However, more

importantly, it is known that high variation within populations severely depresses

FST between populations (Jost 2008). The microsatellites and mtDNA mutual

information index sHUA (Table 4.3) also showed contemporary and historical genetic

differentiation between SCR, OA, and CS that are moderate (0.119 < sHUA < 0.165

and 0.137< sHUA < 0.1221 based on microsatellites and mtDNA markers,

respectively). sHUA avoids dependence of between-population variation on within-

population variation, and relative to FST shows a more robust and predictable

response to levels of dispersal, over a wide range of population sizes and dispersal

rates (Sherwin et al. 2006). The results suggested weak sex-biased dispersal, at least

for females as they may have restricted movement. However, this suggestion was

based on non-significant values (Table 4.5). Other studies found no evidence for sex-


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biased dispersal in bottlenose dolphins that have complex social structure and show

high site fidelity (Natoli et al. 2004; Sellas et al. 2005; Parsons et al. 2006; Rosel et

al. 2009; Ansmann et al. 2012). Here, the four socio-geographic subpopulations

showed different social patterns, with stronger and longer-term social patterns

associated with high residency patterns in the SCR subpopulation and weaker social

patterns with a majority of transient animals in the GR subpopulation (see Chapters 2

and 3, Chabanne et al. 2017a, b).

Contemporary migration rates inferred in BayesAss (Table 4.4) indicated low gene

flow with values estimated at < 4% per generation between socio-geographic

subpopulations. However, the high asymmetric gene flow (0.23 - 0.28) estimated

from OA to other subpopulations suggested that SCR, CS and GR are all genetically

dependent on OA, or the other subpopulations all sometimes use OA’s area, which is

physically intermediate to the other subpopulations’ areas, although different degrees

of demographic independence may occur (see Chapters 2 and 3, Chabanne et al.

2017a, b).

Isolation by distance is not the only factor that can explain genetic differentiation

(e.g., Natoli et al. 2005; Sellas et al. 2005; Gaspari et al. 2015; Allen et al. 2016).

Variation in oceanographic conditions (e.g., salinity and temperature gradients,

habitat type) as well as dissimilarity in prey preference and prey distribution and

abundance, may explain genetic differentiation at a fine-scale (Natoli et al. 2005;

Gaspari et al. 2015). Similarly, Kopps et al. (2014) provided evidence for fine-scale

geographical genetic differentiation driven by socially transmitted behaviour

associated with particular habitat. In my study, however, estimates of low gene flow

between non-differentiated pairwise subpopulations compared with GR (Tables 4.2

and 4.3) may be an artefact of the small sample size representing GR or indicate that

dispersal between populations is at least greater than a few individuals per

generation.

With input from prior information (i.e., the geographic region of the socio-

geographic subpopulations), the Bayesian technique implemented in STRUCTURE did

not reveal the best number of genetic clusters, but varying from one to four

(Appendix A4.2). Although four contemporary genetic clusters seemed to be best


125

when using the method of Evanno et al. (2005), none of the four socio-geographic

subpopulations was strongly assigned to a cluster (proportion of membership q <

0.80, Figure 4.2). The inability of STRUCTURE to correctly assign all individuals to

their subpopulation of origin may be due to software limitations in detecting genetic

differentiation when FST values are low (FST < 0.025, Table 4.2) (Latch et al. 2006).

Relevantly, the same analysis without samples from the SCR locality could not

define more than one genetic population. This result is consistent with the low FST

(values < 0.02, Table 4.2) (Latch et al. 2006) and that the three localities (OA, GR

and CS) in coastal waters define a more continuous environment (i.e., no movement

restriction such as the narrow harbour port at the entrance of the SCR estuary).

Additionally, I previously documented that movement occurs between the three

coastal geographic regions (see Chapter 2, Chabanne et al. 2017b). Louis (2014), for

example, found a similar dilemma in regards to defining more than one genetic

structure among three social clusters.

Estimates of mtDNA FST or ΦST (Table 4.2) were similar, and both were significantly

different to zero only between OA and CS. The dissimilarity in pairwise population

differentiation between estimates of microsatellite FST (biparental inheritance) and

mtDNA FST (maternal inheritance) may be caused by sex-biased dispersal –

however, in this current study, the described weak sex-biased dispersal was not

significant. Another possible explanation, as previously mentioned, is that

microsatellite markers are highly polymorphic (i.e., each locus act as an independent

marker), thus offering higher statistical power than mtDNA markers that are defined

by one locus only (Kalinowski 2002). The lack of differentiation in the mtDNA FST

was also supported by the median-joining network analysis of mtDNA haplotypes

(Figure 4.3) suggesting that the contemporary geographic distributions of individuals

associated with distinct socio-geographic subpopulations was not associated with

historical processes. The analysis described two lineages with shared haplotypes H2

and H3 (clade defining Tursiops aduncus) differing by at least 15 bases from the

other lineage containing another shared haplotype (H1) that was closely related to

Tursiops truncatus or even Stenella coeruleoalba (Genbank). In Shark Bay (WA),

Krützen et al. (2004) found that the microsatellites of the two main haplotype clades

were not significantly different from one another and that interbreeding occurred

between both haplotype clades, but suggested some separation of the matrilines


126

between the eastern Gulf and other localities. In the current study, the most likely

explanation is that the Perth metropolitan waters have been colonised by two distinct

mitochondrial DNA lineages (e.g., other Tursiops aduncus populations along the

south west coast of Western Australia and offshore Tursiops truncatus populations).

4.5.2. Conservation implications

This study found that the genetic structure of Indo-Pacific bottlenose dolphins in

coastal and estuarine waters of Perth seems particularly associated with the current

socio-geographic structure but less clearly with historical events. Indeed, there was

significant differentiation between some of the four socio-geographic subpopulations

for both the FST (Table 4.2) and relatedness index (see Chapter 3, Chabanne et al.

2017a), but the differentiation was weak, thus suggesting a very recent change in

genetic structure or, more likely, that there has never been much genetic structure in

the last 200 years, and the social structure changes every few years, having little

effect on the genetic structure.

Reduction in genetic diversity may occur after a population expansion associated

with a stochastic event such as drastic changes in the environment, selective sweeps

or founder and bottleneck effects (Hedrick 2011). The mtDNA Tajima’s D analysis

(Table 4.7) associated with low nucleotide diversity (π = 0.005, Table 4.6) indicated

the occurrence of a historical bottleneck event or selection in CS (Rand 1996),

although the failure to detect a similar event in SCR may result from low statistical

power of those tests associated with limited sample sizes (Peery et al. 2012).

However, it is important to note that the Tajima’s D result does not agree with all

other bottleneck tests (i.e., for mtDNA: Fu’s Fs; for microsatellites: the stepwise

mutation model (SMM) and the two-phased model of mutation (TPM)). Although

there may have been some bottlenecks, the high gene flow from the OA

subpopulation to others has probably erased any signal. Historically, the OA and CS

embayments as well as the section between Fremantle and Rottnest Island ( < 20 km)

were flooded (c. 5,000 years ago) sometime after Gage Roads (c. 7,000 years ago)

during the rise in sea levels of the Holocene marine transgression (Brearley 2005).

OA may be connected to dolphins at Rottnest Island and from there to dolphins

further offshore (i.e., more T. truncatus types). Thus, gene flow may occur not only


127

north – south along the coast but also offshore – onshore. The connection between

the SCR and coastal areas, however, was more recent with the removal of the rock

bar at the river mouth in the late 1800s (Department of Parks and Wildlife 2015),

although historical research indicates that dolphins were already present in the SCR

(pers. comment S. Graham-Taylor). The SCR is a permanently open estuary with the

middle and upper reaches that changes from brackish (conditions still occurring in

winter and spring) to marine salinity conditions (Department of Parks and Wildlife

2015). In 2009, the subpopulation of bottlenose dolphins in the SCR experienced an

unusual mortality event, with six dolphins found dead (Stephens et al. 2014). In a

report associated with this event, seven haplotypes were identified among 13

samples (obtained between 2007-09) of resident individuals from the SCR

subpopulation (Holyoake et al. 2010; Chabanne et al. 2012). In my study, however, I

defined only four haplotypes (Table 4.6), thus, suggesting a loss of haplotypic

diversity potentially associated with the 2009 unusual mortality event (Stephens et

al. 2014) or

Microsatellite genetic variation in CS was high in comparison to GR and OA (Table

4.6), but could not be explained by a large population size. Instead, contemporary

gene flow from OA (Table 4.4) may have helped to maintain the genetic variation,

although influx from other adjacent populations may occur (see, for example, Figure

4.2b showing considerable admixture with a genetic cluster that was not well

represented in other socio-geographic subpopulations). In that context, it is notable

that some individuals from CS that were reported overlapping with adjacent

populations located further south (c. 12 km, pers. comment K. Nicholson), provide

an opportunity for interbreeding at a larger scale. Additionally, Manlik et al. (in

prep-a) suggested genetic connectivity of bottlenose dolphins from Bunbury (located

c.180 km south of Perth) into northern localities, including Perth.

In addition to the possible bottleneck or founder event, the INEST test suggested

some inbreeding in SCR (Table 4.6). The high site fidelity, year-round residency

pattern, long-term and stable association patterns amongst females and amongst

males, low but stable abundance (i.e., number of individuals using the SCR),

asymmetric demographic movement (see Chapters 2 and 3, Chabanne et al. 2017a,


128

b) and the contemporary gene flow from OA observed for SCR are all characteristics

indicating that SCR acts as a sink and OA as a source in a source-sink dynamic.

4.5.3. Conclusion

Despite there being some current social and spatial patterns in bottlenose dolphins

inhabiting Perth metropolitan waters (Chapter 3, Chabanne et al. 2017a), there is

only weak genetic structure, apparently because of the strong source-sink dynamic

between a coastal subpopulation OA (source) and others in the Perth metropolitan

region (SCR, CS and GR – sinks). This indicates that OA should be accorded very

high conservation status within the Perth metropolitan region.

The SCR is a small subpopulation with fewer than 25 individuals, exhibiting strong

ecological affinity to an estuary where significant anthropogenic stressors occur (see

Chapters 2 and 3, Chabanne et al. 2017a, b), low genetic variation, and restricted

gene dispersal, factors suggesting that inbreeding may occur. The results of this

chapter, along with the findings reported in Chabanne et al. (2012) and Chapters 2

and 3 (Chabanne et al. 2017a, b) of this thesis and those related to the 2009 unusual

mortality event (Holyoake et al. 2010; Stephens et al. 2014) suggest that the SCR

subpopulation is highly vulnerable and that appropriate conservation measures are

needed in addition to maintaining its connectivity with the coastal subpopulation OA

(source).

It is also possible that other adjacent populations from outside the study area

contribute to the genetic material of the bottlenose dolphins in the Perth area.

Although Manlik et al. (in prep-a) recently indicated the occurrence of genetic

dispersal from Bunbury (c. 180 km south of Perth) to northern populations

(including Perth), further studies would still be necessary to investigate the degree of

connection between Perth bottlenose dolphins and other populations at regional-

scale.


129

Appendices

Appendix A4.1. Linkage disequilibrium

Table A4.1.1. Pairwise loci test for linkage disequilibrium over all four

subpopulations. Significant values after Bonferroni correction are in bold (P-value

<0.05).

Locus#1 Locus#2 Com_GR Com_SCR Com_OA Com_CS

P-value SE P-value SE P-value SE P-value SE

DIrFCB4 LobsDi_19 0.0120 0.0005 0.8777 0.001

8

0.5505 0.005

4

0.2788 0.0082 DIrFCB4 LobsDi_21 0.8366 0.0022 0.4797 0.003

8 0.0025 0.000

4 0.5008 0.0104

LobsDi_19 LobsDi_21 0.2357 0.0027 0.0452 0.001

5 0.3302 0.003

9 0.1281 0.0039

DIrFCB4 LobsDi_24 1.0000 0.0000 0.7299 0.004

8 0.7952 0.006

5 0.1386 0.0089

LobsDi_19 LobsDi_24 0.0345 0.0013 0.5651 0.005

7 0.4794 0.007

2 0.5359 0.0081

LobsDi_21 LobsDi_24 0.3489 0.0047 0.4028 0.006

1 0.5194 0.006

0 0.4309 0.0091

DIrFCB4 LobsDi_9 1.0000 0.0000 0.6494 0.003

3 0.9414 0.001

8 0.0371 0.0026

LobsDi_19 LobsDi_9 1.0000 0.0000 0.0014 0.000

3 0.7765 0.003

1 0.0978 0.0028

LobsDi_21 LobsDi_9 0.7722 0.0027 0.1470 0.003

3 0.9787 0.000

7 0.7034 0.0049

LobsDi_24 LobsDi_9 1.0000 0.0000 0.4475 0.006

4 0.1578 0.004

9 0.0424 0.0027

DIrFCB4 DIrFCB5 1.0000 0.0000 0.5115 0.003

6 0.6933 0.004

0 0.8193 0.0065

LobsDi_19 DIrFCB5 0.2996 0.0026 0.3478 0.003

8 0.4304 0.003

6 0.5041 0.0054

LobsDi_21 DIrFCB5 0.4733 0.0034 0.3615 0.003

8 0.2674 0.002

9 0.1435 0.0041

LobsDi_24 DIrFCB5 0.2926 0.0043 0.5887 0.005

6 0.2768 0.005

1 0.2269 0.0068

LobsDi_9 DIrFCB5 1.0000 0.0000 0.9778 0.000

9 0.3481 0.003

2 0.2314 0.0045

DIrFCB4 LobsDi_39 0.5450 0.0034 0.4976 0.003

2 0.6350 0.005

9 0.2544 0.0100

LobsDi_19 LobsDi_39 0.5597 0.0027 0.2305 0.002

8 0.4340 0.004

8 0.3475 0.0067

LobsDi_21 LobsDi_39 1.0000 0.0000 0.1234 0.002

1 0.9655 0.001

2 0.7517 0.0062

LobsDi_24 LobsDi_39 1.0000 0.0000 0.4556 0.005

0 0.1044 0.005

1 0.0484 0.0039

LobsDi_9 LobsDi_39 0.1384 0.0026 0.7440 0.003

3 0.8900 0.002

4 0.0262 0.0016

DIrFCB5 LobsDi_39 0.8210 0.0021 0.2343 0.002

7 0.0026 0.000

3 0.6036 0.0062

DIrFCB4 SCA22 1.0000 0.0000 0.2592 0.004

4 0.5231 0.008

0 0.0109 0.0025

LobsDi_19 SCA22 0.4093 0.0040 0.4103 0.005

2 0.5318 0.006

6 0.0192 0.0023

LobsDi_21 SCA22 1.0000 0.0000 0.8021 0.003

7 1.0000 0.000

0 0.6081 0.0102

LobsDi_24 SCA22 0.0499 0.0031 1.0000 0.000

0 1.0000 0.000

0 1.0000 0.0000

LobsDi_9 SCA22 1.0000 0.0000 0.6999 0.006

4 0.2665 0.005

3 0.2652 0.0079

DIrFCB5 SCA22 0.2445 0.0040 1.0000 0.000

0 1.0000 0.000

0 0.0655 0.0045

LobsDi_39 SCA22 1.0000 0.0000 0.7091 0.004

6 1.0000 0.000

0 0.6037 0.0110

DIrFCB4 SCA9 1.0000 0.0000 1.0000 0.000

0 0.4509 0.008

8 0.5763 0.0132

LobsDi_19 SCA9 1.0000 0.0000 0.1000 0.004

1 1.0000 0.000

0 0.8635 0.0054

LobsDi_21 SCA9 0.5304 0.0041 0.7322 0.005

8 0.1285 0.004

2 0.8137 0.0071

LobsDi_24 SCA9 1.0000 0.0000 1.0000 0.000

0 1.0000 0.000

0 1.0000 0.0000

LobsDi_9 SCA9 1.0000 0.0000 0.0587 0.003

5 0.6186 0.006

7 0.1308 0.0053

DIrFCB5 SCA9 1.0000 0.0000 0.4561 0.007

2 0.5711 0.006

1 0.4311 0.0078

LobsDi_39 SCA9 1.0000 0.0000 1.0000 0.000

0 1.0000 0.000

0 0.8789 0.0061

SCA22 SCA9 1.0000 0.0000 1.0000 0.000

0 0.1454 0.007

8 0.8350 0.0094

DIrFCB4 TexVet7 0.5372 0.0013 0.0040 0.000

2 0.1994 0.003

2 0.0825 0.0027

LobsDi_19 TexVet7 0.7540 0.0010 0.8372 0.001

4 0.8754 0.001

8 0.3985 0.0033

LobsDi_21 TexVet7 0.2634 0.0012 0.2375 0.001

8 0.0557 0.001

2 0.0420 0.0013

LobsDi_24 TexVet7 0.6379 0.0017 0.5260 0.002

9 0.0296 0.001

6 0.0757 0.0022

LobsDi_9 TexVet7 0.1314 0.0010 0.3741 0.002

5 0.0478 0.001

3 0.7639 0.0026

DIrFCB5 TexVet7 1.0000 0.0000 0.0291 0.000

7 0.2657 0.002

2 0.0075 0.0004

LobsDi_39 TexVet7 0.6971 0.0012 0.1153 0.001

1 0.5579 0.003

4 0.4428 0.0040

SCA22 TexVet7 1.0000 0.0000 0.0902 0.001

6 0.3862 0.004

0 0.0174 0.0013

SCA9 TexVet7 0.1363 0.0012 0.5520 0.003

3 0.0351 0.001

6 0.0452 0.0021


130

Appendix A4.2. Mean of the posterior probabilities (LnP(D)) and ΔK statistic

for STRUCTURE

(a) (b)

(c) (d)

Figure A4.2.1. Mean of the estimated posterior probabilities (LnP(D)) and ΔK

statistic (Evanno et al. 2005) over ten replicate runs for values of K = 1-10 using the

Bayesian method in STRUCTURE ((a) and (b)) without prior location and ((c) and

(d)) with prior location.

-4100

-4000

-3900

-3800

-3700

-3600

0 2 4 6 8

Me

an

of L

nP

(D)

0

5

10

15

20

25

0 2 4 6 8

ΔK

-4200

-4100

-4000

-3900

-3800

-3700

0 2 4 6 8

Me

an

of L

nP

(D)

K

0

1

2

3

4

5

0 2 4 6 8

ΔK

K


131

Appendix A4.3. Mean assignment indices (AIc)

Table A4.3.1. Mean assignment indices (AIc) within sexes overall and between

pairwise socio-geographic subpopulations. Outcomes of the Mann-Whitney U test

are given (Z, P-value).

Subpopulation AIc Test

Female Male Z P-value

All 0.240 (0.244) -0.244 (0.417) -0.177 0.860

GR 0.307 (0.355) -0.219 (0.302) 1.056 0.291

SCR -0.105 (0.575) 0.105 (0.520) 0.295 0.768

OA -0.045 (0.500) 0.060 (0.600) -0.213 0.831

CS 0.345 (0.295) -0.345 (0.647) -0.077 0.939




132

Appendix A4.4. Sex-biased dispersal

The differentiation FST is a function of 1/(1+4Nm) (Wright 1931), and mtDNA

effective population size Nmt should represent 0.25 of the microsatellite effective

population size Ne (Avise et al. 1987).

Table A4.4.1. Estimates of the observed and expected 4Nm values for females

(overall).

Microsatellite FST mtDNA FST

Estimate FST 0.019 0.142

Estimate of 4Nm (observed) 51.632 6.042

Estimate of 4Nm (expected) (51.632) 12.908

Note: observed: FST = 1/(1+4Nm); expected Nmt = 0.25 Ne (microsatellites).

The difference obtained between the observed and expected values of mtDNA 4Nm

suggested restricted female movement with a much lower Nm for mtDNA than

expected if both males and females have similar dispersal (m).

I did not calculate the observed and expected for all populations-pairs because some

mtDNA FST values were negative, thus showing ambiguity of the positive values

(same dataset).

I did not verify the observed and expected values of 4Nm for males because the

mtDNA come from the females.


133

Appendix A4.5. Distribution of allelic frequencies

Figure A4.5.1. Distribution of allelic frequencies in the mode shift test for overall

and within each socio-geographic community (GR = Gage Roads (purple); SCR =

Swan Canning Riverpark (red); OA = Owen Anchorage (green); CS = Cockburn

Sound (orange)).

0

0.2

0.4

0.6

0.8

1

0 0.2 0.4 0.6 0.8 1

Pro

po

rtio

n o

f a

llele

s

Allele frequency class

Overall GR SCR OA CS

135

Chapter 5. Population genetic structure and effective population

sizes in Indo-Pacific bottlenose dolphin (Tursiops aduncus) along the

southwestern coastline of Western Australia

5.1. Abstract

Information on genetic factors that contribute to population structure, connectivity

and effective population size (Ne) is important to inform management strategies for

wildlife conservation. In Australia, isolated populations of Indo-Pacific bottlenose

dolphin (Tursiops aduncus) have been documented at local and regional-scales. At a

local scale, there is only weak genetic structure between socially differentiated

subpopulations inhabiting a closed estuary, an embayment and adjacent coastline in

Perth, Western Australia. To address regional-scale genetic structure, I evaluated the

genetic diversity, population genetic structure and contemporary gene flow of

bottlenose dolphins sampled (n = 221) across eight locations spanning c. 1,000 km

along the southwestern coastline of Western Australia, using 25 microsatellite loci.

Measures of genetic diversity were similar across localities (HO = 0.532-0.592; HE =

0.513-0.574), but significant genetic differentiation (FST) occurred between all

localities, except for two localities in Perth that supported a panmictic population.

Contemporary gene flow described two source-sink dynamics with Bunbury acting

as source for the nearest northern (Mandurah) and southern (Busselton) populations

and Augusta acting as source for Albany and Esperance populations. Gene flow

between other localities was negligible (i.e., 95% confidence interval included zero).

The small estimated effective population size of the Perth metapopulation (cNe =

23.0, 95% CI 19.7-27.3) raises conservation concerns, although the estimate may be

biased downward by the small sample size and upward by the occurrence of

immigration (>10%). Despite the larger sample sizes needed to fully investigate

effective population sizes, the current findings have important conservation

management implications for bottlenose dolphins inhabiting the Perth estuary and

embayment sites as well as those along the adjacent coastline.

Chapter 5 – Regional-scale genetic structure

136

5.2. Introduction

Identifying population structure and genetic connectivity is essential for determining

appropriate scales for wildlife conservation and management (e.g., Wiszniewski et

al. 2010; Bilgmann et al. 2014; Sandoval-Castillo and Beheregaray 2015; Yannic et

al. 2015). In both terrestrial and aquatic environments, gene flow may be limited and

populations may become isolated even where geographical or physical barriers are

absent and the mobility capacity of a species is high (Irwin 2002; Taylor 2005;

Brown et al. 2007). If gene flow is limited, populations are vulnerable to loss of

genetic diversity through genetic drift and mutation and to inbreeding depression.

The effective population size (Ne) is an important evolutionary and conservation

parameter that can help in the assessment of the genetic vulnerability of a population.

For example, a population with an estimate Ne < 50 would most likely experience

inbreeding depression, also be indicative of critical status for the population

(Crnokrak and Roff 1999; Palstra and Ruzzante 2008).As the effective population

size (Ne) represents the number of breeders in an idealised population, it may only be

a portion of the census population size (Nc), which suggests that management

strategies should consider both genetic and demographic factors in assessing the

status of a population (Frankham 1995b).

5.2.1. Genetic structure

Developing appropriate conservation strategies for wildlife can be challenging if the

genetic structure at fine- (or local-) and regional-scales is not known. Studies of the

population genetic structure of cetaceans have found a surprising degree of

differentiation despite the lack of obvious geographic barriers in the marine

environment. While some studies described large panmictic populations (e.g., Kiszka

et al. 2012; Moura et al. 2013; Thompson et al. 2016), others populations were

subdivided into multiple genetically differentiated subpopulations that were

restricted to single areas (e.g., Sellas et al. 2005; Ansmann et al. 2012). At larger

scales, such patterns in population genetic structure may be influenced by geographic

isolation, local genetic drift or isolation by distance (Hoelzel 1998).


137

The genetic population structure of Indo-Pacific bottlenose dolphins (Tursiops

aduncus, referred to as ‘bottlenose dolphin’ hereafter) in coastal areas in Australia

exhibits a variety of structure patterns and with a range of factors potentially

affecting it. At fine or local-scales, genetic structure has been described within large

embayments or estuaries along the east coast of Australia as well as in Shark Bay,

Western Australia. Factors such as social behaviour, feeding specialisations, or

human disturbance explained their structure (e.g., Krützen et al. 2004; Wiszniewski

et al. 2010; Ansmann et al. 2012; Kopps et al. 2014). At regional-scales, changes of

environmental conditions were associated with differentiation of bottlenose dolphins

from the Spencer Gulf (Southern Australia) to coastal populations, the nearest of

which was < 100 km from the opening of the Gulf (Bilgmann et al. 2007). Similarly,

heterogeneous environments and local adaptation genetically were associated with

differentiation of resident populations along the New South Wales (NSW) coast with

only 23 km separating two of the populations (Möller et al. 2007; Wiszniewski et al.

2010). Over larger scales (> 100s km of coastline), Allen et al. (2016) documented a

gradual differentiation that followed the isolation by distance model among seven

populations sampled from Beagle Bay to Coral Bay in northwestern Australia.

The population genetic structure for bottlenose dolphins along the southwestern

coastline of Western Australia has not yet been described, other than at local-scale in

Perth (see Chapter 4). However, the ecology of bottlenose dolphins has been

extensively studied in Bunbury (Smith et al. 2013; Sprogis et al. 2015; Smith et al.

2016; Sprogis et al. 2016) and Perth (Finn 2005; Finn and Calver 2008; Finn et al.

2008; Donaldson et al. 2010; Chabanne et al. 2012; Donaldson et al. 2012a, b).

Populations from both localities exhibit year-round and long-term residency patterns

and strong social structures, although emigration outside of the respective study

areas was suggested (Smith et al. 2013; Sprogis et al. 2015, 2016; see Chapters 2

and 3, Chabanne et al. 2017a, b).

A recent study suggests that, historically, the Bunbury population was a genetic

source to adjacent populations located to the north (Perth and Mandurah) and south

(Busselton and Augusta) (Manlik et al. in prep-a). At a fine-scale, the Perth

population showed weak genetic differentiation between socio-geographic

subpopulations in which a source-sink dynamic system was suggested (see Chapters


138

3 and 4, Chabanne et al. 2017a). More specifically, the Swan Canning Riverpark

(SCR) subpopulation in the estuary acts as a sink while a semi-enclosed embayment

subpopulation (OA) acts as a source for SCR and adjacent subpopulations to the

north (GR) and south (CS) (see Chapter 4). Presumably because of this strong

source-sink dynamic, a Bayesian analysis implemented in STRUCTURE (Pritchard et

al. 2000) did not identify a clear genetic structure, with some individuals presenting

ancestry from an unknown population. At a regional-scale, the southern coastline of

WA offers estuaries, embayments and protected bays for bottlenose dolphins (e.g.,

the Swan Canning Riverpark and Cockburn Sound in Perth, the Leschenault estuary

and Koombana Bay in Bunbury, the Blackwood River estuary in Augusta and many

more), suggesting that genetic population structure may also occur throughout this

region.

5.2.2. Effective population size and effective/census population size ratio

In addition to the census population size (Nc, i.e., number of living animals), the

effective population size (Ne) is an important parameter in understanding the ecology

and evolution of natural populations and to inform conservation and management

(Sollmann et al. 2013). The effective population size is the reciprocal of the rate of

genetic change (inbreeding, heterozygosity, linkage disequilibrium) due to random

processes in a finite population. In broad terms, Ne can be thought of as reflecting the

mean number of breeding individuals contributing to offspring per generation. Ne

can have a profound effect on population genetics as it can indicate whether a

population may be at high risk of losing genetic variation (e.g., through genetic drift

or inbreeding) which, in turn, may increase the risk of population extinction (Hare et

al. 2011). Ne is ideally estimated from sex ratio, variation of lifetime reproductive

output, variation of census population size, and other factors.

In an ‘ideal’ population (i.e., equal sex ratio; all animals are equally likely to produce

offspring; mating is random; no immigration, emigration, mutation or selection), the

ratio effective/census population sizes (Ne/Nc) should be close to one (Frankham

1995b). However, there is no reason why Nc and Ne should be the same. Ne can be

larger or smaller than Nc, but Ne estimates are generally lower than Nc (Frankham

1995b; Palstra and Fraser 2012). Comparison of multiple Ne/Nc ratios with variation


139

of some factors such as sex ratio, may help understanding the ecological factors that

drive Ne below Nc, thus enhancing effective conservation and management decision-

making (Kalinowski and Waples 2002).

In general, the necessary data to calculate Ne from sex ratio, variation of lifetime

reproductive output, or variation of census population size, are not available, so it is

necessary to back-calculate Ne from observed effects on genetic patterns (linkage

disequilibrium LD, in the current study). Using LD information, researchers can

estimate a ‘single-sample’ Ne called LD-Ne, which may also give an indication of Ne

for buildup of inbreeding (inbreeding Nb) and for loss of heterozygosity (Waples et

al. 2014).

Population Viability Analysis (PVA) can also be used to assess populations at risk

and only demographic parameters (e.g., population size, reproduction, survival,

Manlik et al. 2016) are required to perform it. However, as demonstrated in Chapter

2 (Chabanne et al. 2017b), estimates of census population size (i.e., abundance) and

other parameters (i.e., apparent survival rate) based on mark-recapture analysis

require a large amount of data which can be expensive and time-consuming to obtain

(Tyne et al. 2016). Further issues may arise because of difficulties in accessing sites

(e.g., in remote areas) or when barriers to gene flow for highly mobile animals are

not known and hence the appropriate scale for a study area is not known (Gagnaire et

al. 2015). For such situations, the ratio Ne/Nc could be used for the purpose of

inferring Nc from Ne for inaccessible populations (Frankham 1995b; Luikart et al.

2010) --although first one must calculate the ratio Ne/Nc from other populations of

same species that are known or assumed to have similar environmental conditions.

There has been an increasing effort applied to estimating Ne for wildlife populations

(e.g., Cronin et al. 2009; Hamner et al. 2012; Brown et al. 2014; Dudgeon and

Ovenden 2015; Zachos et al. 2016) However, only a few studies have aimed to

evaluate Ne for bottlenose dolphin populations (Ansmann et al. 2013) despite the

availability of numerous studies on their population census size (e.g., Chilvers and

Corkeron 2003; Fury and Harrison 2008; Mansur et al. 2012; Smith et al. 2013;

Webster et al. 2014; Sprogis et al. 2016). With the exception of the Bunbury and

Perth dolphin populations (Smith et al. 2013; Sprogis et al. 2016; see Chapter 2,


140

Chabanne et al. 2017b), the census population (Nc) size of other populations along

the southwestern coastline of WA are still unknown.

In this chapter, I first address the current gap in the knowledge about regional-scale

genetic structure for Indo-Pacific bottlenose dolphins along the southwestern

coastline of Western Australia (including Perth) by using 25 microsatellite loci to

assess the current regional-scale population genetic variation and genetic structure as

well as gene flow among eight localities spanning from Perth (WA) to Esperance (c.

1,000 km of coastline). Second, I examined the practicality of the linkage

disequilibrium LD method used to estimate the effective population (Ne) size of the

genetically distinct populations. I also assessed the ratio between effective

population size and census population size (Ne/Nc) to add to the available data for

this species in WA (Ansmann et al. 2013), and discussed whether Ne/Nc ratios have

sufficient generality to be used in calculating Nc from Ne in other WA bottlenose

dolphin populations.


5.3.1. Study site and sample collection

Skin biopsy samples from free-living individual dolphins were collected from 2007

to 2011 along the southwestern coastline of Western Australia. Eight localities were

sampled between Perth (S31.951; E115.860) and Esperance (S33.862, E121.890),

covering about 1,000 km of coastline: the Swan Canning Riverpark (SCR) and

Cockburn Sound (CS/OA; includes individuals from Owen Anchorage (OA));

Mandurah (MH); Bunbury (BB); Busselton (BS); Augusta (AU); Albany (AL); and

Esperance (ES) (Figure 5.1). During dedicated and opportunistic small boat surveys,

samples were taken using a remote biopsy system designed for small cetaceans by

PAXARMS (Krützen et al. 2002). Calves assumed to be less than two years old (i.e.,

based on body length and approximate date of birth) were excluded from biopsy

sampling. Tissue samples were preserved in saturated NaCl 20% dimethyl sulfoxide

until DNA extraction (Amos and Hoelzel 1991).


14

1

Figure 5.1. Map of the sampling sites, southwestern Australia, showing the biopsy sample collection sites for bottlenose dolphins (n = 221) from

eight localities: blues = Perth (PE) including Swan Canning Riverpark (SCR) and Cockburn Sound (CS/OA); black = Mandurah (MH); green =

Bunbury (BB); orange = Busselton (BS); purple = Augusta (AU); grey = Albany (AL); and pink = Esperance (ES).


142

5.3.2. DNA extraction and Microsatellite genotyping

Total genomic DNA was extracted from the samples following standard phenol-

chloroform protocol (Davis et al. 1986) or, alternatively, using the Gentra Puregene

Tissue Kit (Qiagen). Samples were PCR genotyped at 25 polymorphic microsatellite

loci: D22 (Shinohara et al. 1997), KWM12 (Hoelzel et al. 1998a), MK3, MK5,

MK6, MK8, MK9 (Krützen et al. 2001), Tur_E12, Tur_F10, Tur4_66, Tur4_80,

Tur4_87, Tur4_91, Tur4_98, Tur4_105, Tur4_108, Tur4_111, Tur4_117, Tur4_128,

Tur4_132, Tur4_138, Tur4_141, Tur_142, Tur4_153, and Tur4_162, (Nater et al.

2009). Following the method described in Manlik et al. (in prep-b), microsatellite

loci were amplified using the Qiagen Multiplex KitTM (Qiagen) in three multiplex

polymerase chain reactions. PCR products were run on an ABI 3730 DNA

Sequencer (Applied Biosystems) and analysed using GENEIOUS 9.1

(http://www.geneious.com; Kearse et al. 2012). Samples were checked for

duplication (i.e., same microsatellite allele sizes, sex, sample locality, photo-

identification) and some samples were removed from the dataset, making a total

sample of 221.

I used the software Micro-Checker 2.2 (Van Oosterhout et al. 2004) to test for

scoring errors due to stuttering and the presence of large allele dropouts across all

loci and localities. The software INEST 2.0 (Inbreeding/Null Allele Estimation,

Chybicki and Burczyk 2009) was used to estimate the frequency of null alleles at

microsatellite loci within each locality and using a population inbreeding model.

Manlik et al. (in prep-a) found no significant linkage disequilibrium in any of the

locus-pairs. Although Hardy-Weinberg equilibrium (HWE) departures were found

for four loci in two different localities, Manlik et al. (in prep-a) indicated that the

loci which departed from HWE were different in each of the two localities, and the

number of loci that departed was lower than expected, at the 5% significance level.

Therefore, all 25 loci were used in the genetic analysis.


For each locality, genetic diversity was estimated by calculating the mean number of

alleles (NA), observed (HO), expected (HE) and unbiased expected (uHE)


143

heterozygosities (Nei 1978) and the number of private allele (NPA) in GenAlEx 6.5

(Peakall and Smouse 2012). Mean allelic richness (AR) and inbreeding coefficient

(FIS) were calculated using FSTAT 2.9 (Goudet 2001). I also estimated FIS using a

correction for null alleles in INEST.

Microsatellites were tested for evidence of recent bottleneck events using the

software BOTTLENECK 1.2 (Cornuet and Luikart 1996). I assessed the significance

of Wilcoxon sign rank tests for two models of generation of new alleles: the stepwise

mutation model (SMM) and the two-phased model of mutation (TPM; variance = 30,

70% stepwise mutational model, 103 iterations). I also inspected the distribution of

allelic frequencies to detect a mode-shift distortion due to the loss of rare alleles

(Luikart et al. 1998).

5.3.4. Genetic differentiation and population structure

Genetic differentiation between pairs of sampling localities was estimated by FST

values using Arlequin 3.5 (Excoffier and Lischer 2010). Significance testing was

based on 104 permutations in Arlequin. Significance values for multiple comparisons

were adjusted by sequential Bonferroni corrections (Rice 1989). I also tested for

isolation by distance (IBD) by conducting a Mantel test comparing untransformed

pairwise FST with untransformed geographic distances among localities. Geographic

distances between sampling localities were measured in the most direct line through

the water between the approximate centres of the areas where samples were

collected. Significance testing was performed using 104 randomizations in IBDWS

3.2 (Jensen et al. 2005).

I used the Bayesian model-based clustering method STRUCTURE 2.3 (Pritchard et al.

2000) to assess the number of genetic clusters in the dataset (K). Individuals were

assigned to a number of clusters within which Hardy-Weinberg equilibrium and

linkage equilibrium were achieved. Markov chain Monte Carlo (MCMC) runs were

conducted for K values ranging from one to ten, using a burn-in length period of 105

iterations, followed by 10

6 replicates. Ten independent runs were performed for each

K. The analysis was performed with population information (i.e., locality) and using

the correlated frequency and admixture models given the close geographical


144

proximity of some localities. The most likely number of genetically homogeneous

clusters (if K ≥ 2) was determined when the mean log probabilities (LnP(D)) among

K values reached its maximum value. Because the most likely K was not clearly

defined (see results section), I also calculated ΔK, a second-order rate of change of

LnP(D), to confirm the most likely K (Evanno et al. 2005).

5.3.5. Gene flow

I used the program BayesAss 3.0 (Wilson and Rannala 2003) to estimate

contemporary gene flow (< 5 generations). I conducted five independent runs using

107 iterations, a burn-in length of 10

6 and a sampling interval of 10

3 steps. To reach

the recommended acceptance rates of total iterations between 20% and 60%, I

adjusted the values of continuous parameters such as migration rates (m), allele

frequencies (a) and inbreeding coefficient (f) to 0.4, 0.6 and 0.7, respectively. To

confirm a convergence, each run was executed with different seed numbers, and

trace files were examined for consistent oscillations using the software Tracer 1.6

(Rambaut et al. 2013).

5.3.6. Effective population (Ne) sizes

Using the linkage disequilibrium (LD) method implemented in LDNe 1.3 (Waples

and Do 2008), I estimated the contemporary effective population sizes (Ne) of the

populations identified by genetic differentiation and population structure. However,

populations with less than 25 samples were discarded from the analysis because the

LD method is unreliable with small sample size (Waples and Do 2010). Ne estimates

were obtained using the random mating model (Sugg et al. 1996). To avoid bias

caused by rare alleles, I specified a criterion Pcrit for which alleles at lower

frequencies were excluded. The choice of Pcrit depended on the sample size of each

population such that 1/(2n) ≤ Pcrit (Waples and Do 2010). Bias due to overlapping

generations (cNe) was also corrected by adjusting the estimates, following the

method used in other studies (Ansmann et al. 2013; Louis et al. 2014): when Ne was

estimated using Pcrit = 0.02, I adjusted Ne by adding 15% of it; with Pcrit = 0.01, I

added 10%. For comparison, I also calculated cNe for Perth using the dataset from

Chapter 4 (n = 109 samples). Although the number of samples used in Chapter 4 was


145

much larger, loci defined in the dataset were different to those used in this study and

less numerous (i.e., only ten were defined, see Chapter 4 for details). Genetic

laboratory work was performed by two different research groups and at different

time (i.e., laboratory work concluded before 2013 for the dataset used in this

chapter). Tissue samples collected after 2013 (see Chapter 4) were not included in

the database used for the genetic structure of this study.

5.4. Results


A total of 221 samples (including 15 from SCR, 24 from CS/OA, 25 from MH, 85

from BB, 19 from BS, 29 from AU, 14 from AL and 10 from ES) were kept after

checking for duplicates (Table 5.1). No evidence for scoring errors due to stuttering

or large-allele dropout was detected for any of the loci in any of the localities.

Evidence for null alleles was found only for locus MK6 in AL, although the

frequency of null allele was < 0.05 (r = 0.016) and thus considered negligible

(Chapuis and Estoup 2007) (see Appendix A5.1). Manlik et al. (in prep-a) showed

no genotypic linkage disequilibrium (LD) in any pairs of loci in any of the localities,

indicating that the 25 loci were independently inherited. All 25 microsatellite loci

were polymorphic across the entire dataset in each of the localities. Levels of genetic

diversity were similar for each locality with average observed heterozygosities

ranging from 0.53 in AU to 0.59 in three localities (SCR, BB, BS) (Table 5.1).

Allelic richness ranged from 3.28 in AU to 3.70 in CS/OA. I found private alleles in

all populations except MH, with frequencies of private alleles varying from 4% in

SCR, CS/OA, and BS (localities with a single private allele) to 16% in BB. FIS

values were not significantly different from zero in any locality, although the overall

FIS value corrected for null alleles (INEST analysis) was significantly positive,

indicating that there was a deficit of heterozygosity relative to HWE expectations as

expected when pooling distant populations (Table 5.1).


146

Table 5.1. Genetic diversity for microsatellite loci in bottlenose dolphin sampled in

eight localities.

Locality n NA AR NPA HO HE uHE FIS FIS corrected

SCR 15 3.920 3.519 1 0.592 0.553 0.571 - 0.035 NS

0.011 NS

CS/OA 24 4.400 3.697 1 0.579 0.574 0.587 0.006 NS

0.016 NS

MH 25 4.200 3.390 0 0.536 0.547 0.559 0.041NS

0.025 NS

BB 85 4.520 3.451 4 0.586 0.565 0.568 - 0.031NS

0.005 NS

BS 19 3.880 3.287 1 0.591 0.532 0.546 - 0.062 NS

0.007 NS

AU 29 4.160 3.279 3 0.532 0.534 0.543 0.020 NS

0.017 NS

AL 14 3.600 3.556 2 0.554 0.513 0.533 - 0.040 NS

0.009 NS

ES 10 3.680 3.628 3 0.568 0.525 0.554 - 0.026 NS

0.018 NS

Overall 221 4.045 4.235 15 0.567 0.543 0.558 - 0.008 NS

0.016 *-

Note: n = number of samples, NA = mean number of alleles, AR mean allelic richness,

NPA = number of private alleles, HO = observed heterozygosity, HE = expected

heterozygosity, uHE = unbiased expected heterozygosity, FIS = inbreeding coefficient

(all tested using Bonferroni correction: NS = non-significant at P-value > 0.05), FIS

corrected = inbreeding coefficient corrected for null alleles (NS = non-significantly

different from zero, * = significantly different from zero). SCR = Swan Canning

Riverpark, CS/OA = Cockburn Sound, PE = Perth, MH = Mandurah, BB = Bunbury,

BS = Busselton, AU = Augusta, AL = Albany, ES = Esperance.

Results from BOTTLENECK indicate an excess of heterozygosity (one-tailed

Wilcoxon test for heterozygosity excess relative to numbers of alleles, which are lost

faster than heterogeneity in a bottleneck (P-value < 0.05 after Bonferroni correction,

Appendix A5.2), which suggests a recent reduction in effective population size of

SCR and CS/OA pooled, and BB. Additionally, the graphical representation of the

allele frequencies for all 25 polymorphic loci (see Appendix A5.2) shows a deficit of

rare alleles (i.e., frequency < 0.1) in AL and ES, causing a mode-shift distortion (i.e.,

indication of recently bottlenecked populations, Luikart et al. 1998). The

representation for all other localities did not show a typical ‘L-shape’ because more

alleles were found in intermediate frequency classes than in the low frequency class.

5.4.2. Genetic differentiation and population structure

All FST pairwise comparisons were significant (Table 5.2) with FST values varying

from 0.026 to 0.113, except between SCR and CS/OA where FST was low and non-

significant after sequential Bonferroni correction (FST = 0.008, P-value = 0.0123).


147

The Mantel test revealed a positive and significant correlation between FST and

geographic distances (R = 0.58, P-value <0.02, Appendix A5.3) among all sample

localities. Geographic distance accounted for 34% of the variation in genotypic

distance. In detail, I found significant isolation by distance (R = 0.89, P-value < 0.01,

Appendix A5.3) between localities bordering the Indian Ocean (SCR, CS/OA, MH,

BB and BS) with geographic distance accounting for 80% of the variation in

genotypic distance. However, the test was non-significant between localities

bordering the Great Australia Bight (AU, AL and ES, R = -0.93, P-value = 0.84,

Appendix A5.3).

Table 5.2. Genetic differentiation (FST) among eight bottlenose dolphin sampling

localities in southern Western Australia.

SCR CS/OA MH BB BS AU AL

SCR -

CS/OA 0.008*** -

MH 0.039*** 0.030*** -

BB 0.045*** 0.043*** 0.026*** -

BS 0.064*** 0.065*** 0.047*** 0.029*** -

AU 0.028*** 0.033*** 0.061*** 0.060*** 0.060*** -

AL 0.059*** 0.074*** 0.113*** 0.107*** 0.083*** 0.057*** -

ES 0.075*** 0.051*** 0.096*** 0.081*** 0.072*** 0.045*** 0.060***

Note: * P-value < 0.05, ** P-value < 0.01, *** P-value < 0.001 after sequential

Bonferroni correction. SCR = Swan Canning Riverpark, CS/OA = Cockburn Sound,

MH = Mandurah, BB = Bunbury, BS = Busselton, AU = Augusta, AL = Albany, ES

= Esperance.

The Bayesian clustering analysis implemented in STRUCTURE showed clustering of

bottlenose dolphins from the southwestern coastline of Western Australia. Firstly,

the mean posterior probability (LnP(D)) reached a plateau at K = 4 but slightly

increased up to K = 7 clusters (Appendix A5.4). The ΔK index obtained by the

method of Evanno et al. (2005) indicated two modes at K = 2 and K = 4 (Appendix

A5.4). At K = 2 (Figure 5.2a), all individuals sampled in BB were strongly assigned

to one genetic cluster (assignment probability q > 0.95) while all other localities

were assigned to a second cluster (0.51 < q < 0.97). At larger K value (K = 4, Figure

5.2b), only three genetic clusters seemed relevant (i.e., sporadic representation of the

fourth genetic cluster). Individuals sampled in SCR and CS/OA formed one cluster


148

(northern cluster with q > 0.70 for 72% of the individuals) while AU, AL and ES

formed a second cluster (southern cluster), both clusters being distinctive to the BB

genetic cluster. Individuals from MH and BS were of mixed ancestry between BB

and the northern genetic cluster or the southern genetic cluster, respectively.

Secondly, because of the long distance that separated the southern localities from

one another, I ran an independent Bayesian clustering analysis to provide higher

statistical power for detecting genetic clusters among AU, AL and ES localities. The

LnP(D) and ΔK index was higher at K = 3 (LnP(D) = -2731.28, see Figures A5.4C

and D), with majority of the individuals from each locality being assigned to a

respective genetic cluster (Figure 5.2c). Altogether, STRUCTURE identified five

populations, including SCR and CS/OA pooled into one population (referred to as

Perth – PE hereafter). However, because of the admixture found in individuals from

MH and BS and the moderate levels of genetic differentiation with BB (FST ≈ 0.03,

Table 5.2), populations MH and BS were considered to be two distinct populations

for subsequent analyses.


149

Figure 5.2. Structure plots showing assignment probability of each dolphin (one

individual per column) to the respective populations for different number of clusters,

K: (a) with K = 2 and (b) K = 4 for the full dataset (n = 221); and (c) with K = 3 for

Augusta, Albany, and Esperance only (n = 53). PE = Perth (including SCR = Swan

Canning Riverpark and CS/OA = Cockburn Sound), MH = Mandurah, BB =

Bunbury, BS = Busselton, AU = Augusta, AL = Albany, ES = Esperance.


150

5.4.3. Contemporary gene flow

Based on the seven populations (i.e., including MH and BS as distinct populations)

identified from the FST differentiation and STRUCTURE analyses, the BayesAss

analysis of microsatellite genotypes (Table 5.3) indicated high migration from BB

into MH (located c. 80 km north from BB) or BS (c. 40 km south from BB) with

estimates of 24% and 20% migrants per generation, respectively. Similarly, there

was high migration from AU to the southern localities with 23% and 18% of

migrants per generation into AL (c. 300 km east from AU) and ES (c. 700 km east

from AU), respectively. Other pairwise comparisons indicated lower migration rates

ranging from 9% to > 1%, with the majority showing a 95% confidence interval that

included zero. Specifically, contemporary migration from and into PE was little or

negligible (i.e., 95% confidence interval included zero). The proportion of non-

immigrants was the highest in BB (97%) following by PE (86%) and AU (85%).


15

1

Table 5.3. Mean (and 95% CI) recent migration rates inferred using BayesAss 3.0 (Wilson and Rannala 2003). The migration rate is the

proportion of individuals in a population that immigrated from a source population per generation. Values of CI that do not overlap with zero are

in bold. Values along the diagonal (underlining) are the proportions of non-immigrants from the origin subpopulation for each generation.

From

Into PE MH BB BS AU AL ES

PE 0.86 0.01 0.04 0.01 0.05 0.01 0.02

(0.79-0.93) (0.00 -0.03) (0.00-0.10) (0.00-0.02) (0.00 -0.11) (0.00-0.02) (0.00-0.05)

MH 0.03 0.68 0.24 0.01 0.02 0.01 0.01

(0.00-0.11) (0.66-0.70) (0.18-0.29) (0.00-0.02) (0.00-0.08) (0.00-0.02) (0.00-0.04)

BB 0.01 0.01 0.97 0.00 0.01 0.00 0.00

(0.00-0.08) (0.00-0.03) (0.90-1.00) (0.00-0.02) (0.00-0.065) (0.00-0.02) (0.00-0.03)

BS 0.01 0.01 0.20 0.68 0.070 0.01 0.01

(0.00-0.09) (0.00-0.03) (0.14-0.25) (0.67-0.69) (0.01-0.13) (0.00-0.03) (0.00-0.04)

AU 0.02 0.01 0.09 0.01 0.85 0.01 0.02

(0.00-0.09) (0.00-0.03) (0.03-0.14) (0.00-0.02) (0.79-0.91) (0-0.02) (0.00-0.05)

AL 0.02 0.02 0.02 0.02 0.23 0.68 0.02

(0.00 -0.09) (0.00-0.04) (0.00-0.07) (0.00-0.03) (0.17-0.29) (0.67-0.70) (0.00-0.05)

ES 0.03 0.02 0.042 0.02 0.18 0.02 0.69

(0.00-0.10) (0.00-0.04) (0.00-0.10) (0.01-0.03) (0.12-0.24) (0.01-0.03) (0.66-0.72)

Note: PE = Perth, MH = Mandurah, BB = Bunbury, BS = Busselton, AU = Augusta, AL = Albany, ES = Esperance.


152

5.4.4. Effective population sizes

Using the genetic linkage disequilibrium method and correction for bias introduced

by overlapping generations, the corrected effective population sizes (cNe) per

locality varied from 23.0 (95% CI 19.7-27.3) in PE1 (i.e., PE locality using data

from this chapter, n = 39 samples, 25 loci) to 104.6 (95% CI 85.1-132.3) in BB

(Table 5.4). For comparison, I obtained an estimate cNe of 75.8 (95% CI 64.6-90.2)

for PE2, i.e., PE locality when using a larger sample size but fewer loci (n = 109

samples genotyped for 10 loci, see Chapter 4). However, there was a high positive

correlation between the sample size n and Ne or cNe (R1 = 0.8830, P-value < 0.05 and

R2 = 0.8723, P-value = 0.05, respectively, see Appendix A5.5). Both slopes were

near unity (slope1 = 0.8384 and slope2 = 0.8925), which means perfect prediction.

A seasonal mean census population size (Nc) is known for two populations, being

153.8 (95% CI 114.3-193.3) and 148.2 (95% CI 127.5-168.8) adults and juveniles

(i.e., excluding calves) for PE and BB, respectively (i.e., means of the seasonal

abundances estimated using the dataset described in Chapter 2, Chabanne et al.

2017b (but see Appendix A5.6), and in Sprogis et al. 2016, respectively). Nc values

were not corrected for generation overlap as I did for Ne values.

When using a large sample size (n > 50), the mean ratios cNe/Nc between PE (n =

109, PE2) and BB (n = 85) were in the same range with 0.75 (SE 0.20) and 0.71 (SE

0.10), respectively.

On the assumption (discussed further below) that cNe/Nc ratios can be transferred

between populations of this species in WA, I calculated Nc from cNe for two other

populations. I used the cNe/Nc ratio from BB because it was based on the same

markers as the populations in question. For MH, the estimate of census population

size (Nc) based on cNe/Nc could range between 53.7-155.5. For AU, the estimate of

census population size (Nc) based on cNe/Nc ratio could range between 52.3-125.8.


15

3

Table 5.4. Estimates of the contemporary effective population size before correction (Ne, 95% CI) and after correction (cNe, 95% CI), ratio

cNe/Nc (SE) for localities with known census population size (Nc, , 95% CI), and estimated eNc (95% CI) for populations with unknown Nc. The

critical value varied according to the sample size (n > 25, Pcrit = 0.02; n > 50, Pcrit = 0.01). cNe was corrected for overlapping generation by

adding 15 or 10% of the estimates based on the Pcrit, respectively.

Locality n Pcrit

Estimates of sample size

Nc * cNe/Nc eNc

estimated from cNe/Nc Ne

cNe

PE1 39 0.02 20.0 23.0 153.8 0.15

- (17.1-23.7) (19.7-27.3) (114.3-193.3) (SE 0.04)

PE2 109 0.01 105.1 115.6 153.8 0.75

- (77.9-129.0) (85.7-141.9) (114.3-193.3) (SE 0.01)

MH 25 0.02

50.6 58.2 unknown -

82.0

(33.1-96.0) (38.1-110.4 ) (53.7-155.5)

BB 85 0.01

95.1 104.6 148.2 0.71 -

(77.4-120.3) (85.1-132.3) (127.5-168.8) (SE 0.10)

AU 29 0.02 46.8 53.8

unknown - 75.8

(32.3-77.7) (37.1-89.3) (52.3-125.8)

Note: n = sample size; PE1 = Perth using the current study dataset; PE2 = Perth using the dataset described in Chapter 4, MH = Mandurah, BB =

Bunbury, AU = Augusta. * mean of the seasonal abundances were estimated using abundances estimated in the Chapter 2 (Chabanne et al.

2017b) and in Sprogis et al. (2016).


154

5.5. Discussion

This study provides a key regional-scale contribution to the understanding of the

distribution of genetically differentiated populations of Indo-pacific bottlenose

dolphins in nearshore areas in Western Australia (Bilgmann et al. 2007; Wiszniewski

et al. 2010; Allen et al. 2016). It also investigated whether the LD method can be

used in WA for Tursiops species to calculate the ratio between effective Ne

(corrected for generation overlap) and census Nc population size not corrected for

generation overlap, a parameter considered as of interest to scientists, managers and

governmental agencies.


Genetic diversity of bottlenose dolphins along the southwestern coastline of Western

Australia was moderate and similar between each locality (Table 5.1). Estimates of

the observed (HO) and expected (HE) heterozygosities were found to be in the same

range as those obtained for other populations along the north western coastline of

WA (HO 0.538-0.667; HE 0.547-0.613, Allen et al. 2016) as well as along the east

coast of South Africa (HE 0.54-0.59, Natoli et al. 2007). Wiszniewski et al. (2010)

also estimated HO of 0.54-0.59 for bottlenose dolphins inhabiting the Port Stephens

(New South Wales, Australia). However, those values were the lowest in comparison

to adjacent populations along the New South Wales coast.

It is generally expected that after a population colonises a new habitat (which may

also be described as a founder event, e.g., when an estuary becomes accessible from

an adjacent coastline), there will be a loss of microsatellite diversity while the

original population maintains its level of genetic diversity (Hoelzel 1998; Hoffman

et al. 2009). Here, none of the study populations showed differences in microsatellite

genetic diversity, suggesting that a recent founder event has not occurred. However,

tests run in BOTTLENECK and the documented allele frequency distribution

(Appendix A5.2) indicated that all populations went through a recent reduction of

effective population size (Luikart et al. 1998). Although the results may be explained

by individuals becoming more genetically closely related to individuals within than

between populations (Moura et al. 2013), it is more likely that a continuing


155

population decline is occurring at regional-scale. If declining, the viability of the

populations along the southwestern coastline of WA might be of very high concern,

in particular since Manlik et al. (2016) indicated that BB population was projected to

decline and at risk of extinction.

5.5.2. Genetic differentiation and structure

Pairwise comparisons based on FST (Table 5.2) showed significant differences in

nuclear DNA between all localities except between SCR and CS/OA. The lack of

differentiation between SCR and CS/OA is in general agreement with the previous

study (see Chapter 4) in which genetic differentiation between the socio-geographic

subpopulations in the Perth metropolitan region was weak, although CS/OA samples

in this current study included individuals from two of the previously defined socio-

geographic subpopulations (OA and CS, Chapter 3, Chabanne et al. 2017a). The

identification of individuals from OA (i.e., source population for SCR and CS as

defined in the Chapter 4) supported the gene flow from the CS/OA locality defined

in the current study to the SCR and may explain the negligible genetic differentiation

(microsatellite FST value of 0.008 in this current study, Table 5.2) in comparison to

those found in Chapter 4 (microsatellite FST values of 0.023-0.025, P-value < 0.05,

Table 4.4).

As for many mammal species (terrestrial or marine, e.g., Olsen et al. 2014; Yannic et

al. 2015; Allen et al. 2016), the genetic population structure of bottlenose dolphins

throughout the study area was characterised by a pattern of isolation by distance

(IBD) (Appendix A5.3). The values of microsatellite FST (Table 5.2), as well as

mtDNA FST values (Manlik et al. in prep-a), clearly showed an increase of

differentiation with an increase of geographic distance from CS/OA to BS, the

southernmost of the western coastline populations (Figure 5.1). However, values of

FST between SCR or CS/OA and AU were lower despite a larger geographic distance

(> 250 km) suggesting a closer ancestral link between those three localities.

Alternatively, the bottlenose dolphins’ high mobility or ranging pattern allows them

to travel long distances without interacting with populations that are closer

geographically (e.g., if individuals moved along the coast in offshore areas, away

from nearshore populations), thus preventing the development of a strong


156

relationship between gene flow and geography. At a finer-scale, the IBD model

(Appendix A5.3) did not explain the differentiation with localities bordering the

Great Australian Bight, and all localities, except SCR, showed a larger

differentiation from AL than ES (the easternmost locality). It is difficult to explain

such differentiation patterns and it may be related to the respective small sample

sizes (< 20 samples) affecting the power of the analysis. However, the overall IBD

model for all localities along the southwestern coastline of WA was not rejected

(Figure A5.2).

STRUCTURE analyses indicated a clear genetic population structure that

differentiated the BB population from others (K = 2, Figure 5.2a and Appendix

A5.4). However, genetic structure on a finer scale was apparent to the north and the

south of the BB population, either by significant genetic differentiation FST between

neighbour populations (Table 5.2) or by genetic admixture patterns (Figure 5.2b).

This finding provides the basis for the recommendation for distinguishing seven

populations. Two further points may be made. First, individuals from SCR and

CS/OA were mostly assigned to one genetic cluster, which was in agreement with

the weak to negligible FST value (FST < 0.01, P-value > 0.05, Table 5.2). Secondly,

individuals from MH and BS were not assigned to one genetic cluster but showed

some admixture in which BB represented one ancestral population. Both MH and BS

showed a weak but significant differentiation from the central population at BB (FST

= 0.026 and 0.029 with P-value < 0.001, respectively, Table 5.2). However,

presumably because FST values < 0.03, no distinct genetic cluster was detected in

STRUCTURE (Evanno et al. 2005; Latch et al. 2006).

5.5.3. Contemporary gene flow

Analysis of contemporary migration rates using BayesAss (Table 5.3) estimated

significant rates of gene flow (i.e., different from zero) from BB to MH and BS as

well as from AU to AL and ES. However, migration estimates on the opposite

direction were not significant, suggesting asymmetrical migration between these

pairs of populations. Migration rates of individual dolphins between all other pairs of

populations were negligible. The contemporary migration pattern associated with BB


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was similar to that found historically (i.e., across tens to hundreds of generations,

Manlik et al. in prep-a).

5.5.4. Contemporary effective population size and Ne/Nc ratio

Contemporary effective population sizes (Ne, Table 5.4) were much larger for the

BB population than for others, although this apparent difference is probably due to

the relatively low sample size representing the MH and BS populations (Waples and

Do 2010). Indeed, I found that Ne calculating using Waples and Do (2008) LDNe

method or the corrected parameter (cNe) were highly correlated with the sample size

(N, see Appendix A5.5). In comparison to Ne estimates for the MH and BS

populations, the estimate of Ne for the PE population (SCR and CS/OA combined)

was lower than its sample size with a narrow confidence interval. Despite both the

PE and BB populations having similar genetic diversities (Table 5.1) and having

been through a bottleneck event (i.e., reduction of Ne in both populations, although

the method is very dependent on chance, Luikart et al. 1998), the difference of

sample size has a clear effect on Ne estimates. When using a larger sample size (n =

109, see Chapter 4), the Ne estimate for the PE population (cNe = 115.6, 95% CI

85.7-141.9) was similar to the Ne estimated for the BB population despite using

fewer loci (ten loci, see Chapter 4). Other studies have shown that increasing the

numbers of samples could have a greater effect on precision than increasing the

number of loci (Dudgeon and Ovenden 2015). However, the comparison may not be

adequate because of the use of different markers (i.e., microsatellites but with

different loci). Ne estimated for the PE population in this study should be considered

as a worst case scenario because a value of Ne < 50 describes populations at high risk

of inbreeding depression, and thus extinction (Crnokrak and Roff 1999; Palstra and

Ruzzante 2008).

The interpretation of Ne estimated via the linkage disequilibrium method in LDNe

(Waples and Do 2008) is strongly influenced by the sample size as well as several

biological features, including overlapping generations and gene flow between

populations (Hare et al. 2011; Waples and England 2011; Waples et al. 2014). These

issues suggest the following points in relation to this study.


158

First, I must correct for overlapping generations. Although the data were collected

over a period of only five years, there is a high chance that multiple generations were

sampled, as bottlenose dolphins are long-lived animals with low fecundity, late

maturity and high survivorship to adult stages (Wells and Scott 2009). The Ne

estimates obtained are therefore likely to be downwardly biased (Waples and Do

2010; Waples et al. 2014). Following methods described in Waples et al. (2014) and

applied in several other studies (e.g., Ansmann et al. 2012; Brown et al. 2014; Louis

et al. 2014), I corrected the estimates for a 10-15% downward bias (cNe), depending

on the sample size of respective populations. Despite this correction, the estimates

did not change much.

Second, I must deal with immigration (i.e., gene flow), which can result in reduced

Ne values because of an apparent increase in the LD signal amongst a mixed sample

from the recipient population in which immigrants and residents are genetically

differentiated (Waples and England 2011). However, the bias from the “no

immigration” assumption can be considered negligible if the immigration rate (i.e.,

gene flow) is below a 10% threshold (Hastings 1993) and with the LDNe analysis

performing adequately up to this threshold (Waples and England 2011). Based on the

contemporary gene flow analysis implemented in BayesAss (Table 5.3), the BB

population was the only population with an immigration rate falling under the 10%

threshold (immigration rate < 3%), suggesting that only the cNe for BB could be

described as unbiased. In line with the discussion above, the occurrence of

immigration (> 14%) in the PE population would suggest that in addition to the bias

associated with the sample size, the cNe estimate is also biased downwards due to

immigration (Waples and England 2011).

Third, the estimate of Ne can be severely downwardly biased by sample size.

Apparent LD is equally sensitive to random processes in the actual population

(which is what one aims to assess), and random processes during sampling (which,

of course, one does not want to assess). Therefore unless the sample is much larger

than the likely Ne, sample size can have a serious effect on the Ne estimate. In this

case, the sample size clearly dominated the estimate, to the extent that the sample

size was an almost perfect predictor of the Ne or the Ne corrected for overlap (cNe).

Thus, these Ne estimates are highly unreliable, to say the least.


159

Census population (Nc) sizes for the BB and PE populations were obtained for

periods covering 2007-2013 in BB (Sprogis et al. 2016) and 2011-2015 in PE (see

Chapter 2, Chabanne et al. 2017b), and thus included overlapping generations. My

attempt in producing a Ne/Nc ratio, therefore allows correlation from Ne corrected for

generation overlap (cNe) and Nc that is not corrected for generation overlap. While

defining a constant value for a Ne/Nc ratio may be a particularly valuable tool for

wildlife conservation efforts at the population-level, temporal fluctuations occur in

effective size and, thus, also in the Ne/Nc ratio (Palstra and Ruzzante 2008). In this

study, cNe/Nc ratios were 0.71 for BB and 0.75 for PE populations – however, I

acknowledge that: 1) sample size largely influences the estimates of Ne and 2) Ne

estimates should apply to Nc from the previous generation if overlapping generations

during sampling did not occur (Palstra and Fraser 2012). In addition, the estimate for

the PE population was obtained using another dataset (i.e., more skin samples but

fewer and different loci).

As in other marine animals (e.g., elasmobranchs, Dudgeon and Ovenden 2015), the

ratio Ne/Nc for delphinids is expected to be near to one (Frankham 1995b; Portnoy et

al. 2009). Their longevity along with low fecundity associated with long gestation

and nursing periods, late maturity and high survivorship parameters may maintain Ne

close to Nc (Portnoy et al. 2009). On that basis, the lower ratios found in this study

suggest that populations along the southern coastline of WA may have suffered from

bottleneck events (i.e., reduction of Ne, Luikart et al. 1998). It is difficult to interpret

the Ne/Nc ratio without the availability of other studies for comparison, with the

exception of the study by Louis et al. (2014) which reported a Ne/Nc ratio of 5-10%

and therefore proposed that the bottlenose dolphin population inhabiting the coastal

areas of Atlantic Ocean and the Mediterranean Sea was in a critical state.

Despite those issues, the Ne/Nc ratio has been suggested as a tool to estimate the

census population size of other populations (Frankham 1995b; Luikart et al. 2010).

Nonetheless, caution is required because of several unresolved assumptions and the

different factors affecting populations (e.g., habitat factors or expansion and

contraction). In this study, for example, census population size for MH and BS had

values ranging from 53.7 to 155.5 and 52.3 to 125.8, respectively. However,


160

immigration (> 30%, Table 5.3) occurred and the respective sample sizes were small,

both downwardly biasing the cNe estimates.

5.5.5. Conclusions and future research

My study suggests that present bottlenose dolphin genetic structure patterns along

the southwestern coastline of Western Australia may consist of two metapopulations.

In the Indian Ocean, BB acts as a genetic source for the nearest northern (MH) and

southern (BS) populations. In the Great Australian Bight, AU acts as a genetic

source for AL and ES populations, although limited genetic connectivity was also

found between AU and BS (c. 120 km apart). Although results from the bottleneck

tests and the distribution of allele frequencies suggest a general decline of bottlenose

dolphins in the southwestern region, such declines have only been projected from

demographic data for BB population itself (Manlik et al. 2016). Such a trend would

have a significant implication for evaluations of the vulnerability of the species at

regional-scale and highlights the importance of continued monitoring of these

populations, and of obtaining larger genetic sample size for more reliable results.

The population of bottlenose dolphins in Perth was defined by the presence of a

panmictic pattern between the SCR and CS/OA. In addition, it is possible that the

genetic connectivity between PE and other populations may have changed within the

last few generations. While historically the PE population appears to have obtained

asymmetric immigration from the BB population (Manlik et al. in prep-a), this study

found little to no contemporary connectivity.

The current approach for estimating the effective population size (Ne) and the ratio

effective and census population sizes (Ne/Nc) still possesses several critical

limitations that must be resolved before the approach can be of broad utility. Thus,

despite the high conservation management interest in the development of methods to

estimate Ne and Ne/Nc, it appears that the LD method used in this study does not

estimate the effective population size but instead is highly correlated with the sample

size. Even if the sample size issue is set aside, the assumptions (i.e., overlapping

generations and migration) associated with the methods to calculate Ne and Ne/Nc

still are largely violated with bottlenose dolphins. In addition, while Waples et al.


161

(2014) methods may assist in minimising biases due to generation overlapping, the

migration assumption and sample size dependence are still problematic. The

difference found between the Ne estimated for PE population using two different

datasets also suggested the need to understand better the limits associated with the

choice of the markers (e.g., loci).


162

Appendices

Appendix A5.1. Genetic diversity for 25 microsatellite loci

Table A5.1.1. Genetic diversity indices for eight sampling localities for all 25

microsatellite loci. n = number of individuals, NA = number of alleles, HO = observed

heterozygosity, HE = expected heterozygosity, FIS = inbreeding coefficient, r =

frequency of null alleles. SCR = Swan Canning Riverpark, CS/OA = Cockburn

Sound, MH = Mandurah, BB = Bunbury, BS = Busselton, AU = Augusta, AL =

Albany, ES = Esperance.

Locality Locus n NA HO HE FIS r (95% CI)

SCR MK6 15 7 0.733 0.798 0.081 -0.001 0.004

Tur4_117 15 4 0.467 0.490 0.049 -0.002 0.010

Tur4_98 15 2 0.400 0.329 -0.217 -0.001 0.006

Tur4_66 15 3 0.200 0.248 0.192 -0.002 0.010

E12 15 3 0.800 0.640 -0.249 -0.001 0.003

Tur4_108 15 2 0.533 0.457 -0.167 -0.001 0.007

Tur4_128 15 3 0.467 0.481 0.030 -0.001 0.007

Tur4_111 15 3 0.067 0.195 0.659 -0.004 0.035

Tur4_105 14 7 0.857 0.835 -0.026 -0.002 0.017

D22 15 2 0.733 0.500 -0.467 -0.001 0.005

Tur4_87 15 3 0.800 0.655 -0.222 -0.001 0.004

Tur4_91 15 5 0.733 0.757 0.031 -0.001 0.006

Tur4_138 15 4 0.800 0.745 -0.073 -0.001 0.004

Tur4_141 15 7 0.867 0.824 -0.052 -0.001 0.004

F10 15 4 0.733 0.576 -0.273 -0.001 0.003

MK8 15 6 0.600 0.712 0.157 -0.001 0.007

MK3 15 4 0.733 0.605 -0.213 -0.001 0.003

KWM12 15 6 0.733 0.579 -0.267 -0.001 0.004

MK9 15 4 0.533 0.679 0.214 -0.002 0.013

MK5 15 3 0.333 0.543 0.386 -0.004 0.032

Tur4_153 15 2 0.400 0.519 0.229 -0.002 0.012

Tur4_80 15 5 0.533 0.662 0.194 -0.002 0.009

Tur4_132 15 2 0.133 0.129 -0.037 -0.002 0.009

Tur4_142 15 3 0.800 0.640 -0.249 -0.001 0.003

Tur4_162 15 4 0.800 0.693 -0.155 -0.001 0.004

CS/OA MK6 23 8 0.783 0.763 -0.026 -0.001 0.005

Tur4_117 24 4 0.417 0.565 0.263 -0.003 0.023

Tur4_98 24 2 0.375 0.361 -0.040 -0.001 0.008

Tur4_66 24 3 0.292 0.263 -0.110 -0.001 0.007

E12 22 4 0.545 0.696 0.216 -0.005 0.048

Tur4_108 24 2 0.375 0.361 -0.040 -0.001 0.008


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CS/OA Tur4_128 21 3 0.286 0.370 0.228 -0.006 0.051

Tur4_111 21 4 0.429 0.361 -0.188 -0.002 0.011

Tur4_105 20 7 0.900 0.818 -0.100 0.000 0.002

D22 24 3 0.750 0.578 -0.298 -0.001 0.003

Tur4_87 24 3 0.625 0.582 -0.075 -0.001 0.005

Tur4_91 24 6 0.708 0.742 0.045 -0.001 0.008

Tur4_138 24 4 0.625 0.645 0.031 -0.001 0.005

Tur4_141 24 7 0.875 0.732 -0.196 0.000 0.002

F10 20 5 0.750 0.789 0.050 -0.002 0.010

MK8 24 6 0.750 0.687 -0.092 -0.001 0.004

MK3 24 5 0.708 0.722 0.019 -0.001 0.005

KWM12 24 6 0.625 0.675 0.074 -0.001 0.006

MK9 24 5 0.500 0.612 0.183 -0.002 0.014

MK5 24 5 0.708 0.701 -0.010 -0.001 0.007

Tur4_153 23 2 0.478 0.512 0.066 -0.003 0.026

Tur4_80 22 5 0.682 0.708 0.037 -0.002 0.008

Tur4_132 20 2 0.250 0.224 -0.118 -0.004 0.024

Tur4_142 22 3 0.591 0.561 -0.054 -0.002 0.012

Tur4_162 20 5 0.600 0.671 0.106 -0.003 0.020

MH MK6 25 8 0.800 0.792 -0.011 -0.001 0.008

Tur4_117 25 4 0.560 0.542 -0.032 -0.001 0.010

Tur4_98 25 2 0.400 0.492 0.186 -0.003 0.023

Tur4_66 25 3 0.120 0.117 -0.029 -0.002 0.015

E12 24 4 0.625 0.650 0.039 -0.003 0.029

Tur4_108 25 2 0.280 0.245 -0.143 -0.002 0.012

Tur4_128 22 4 0.500 0.582 0.141 -0.005 0.044

Tur4_111 22 2 0.182 0.169 -0.077 -0.007 0.069

Tur4_105 20 7 0.650 0.763 0.148 -0.002 0.018

D22 25 3 0.640 0.601 -0.065 -0.001 0.009

Tur4_87 25 3 0.280 0.383 0.270 -0.003 0.023

Tur4_91 25 8 0.800 0.784 -0.020 -0.001 0.006

Tur4_138 25 4 0.840 0.685 -0.226 -0.001 0.004

Tur4_141 25 7 0.720 0.757 0.048 -0.001 0.007

F10 23 5 0.783 0.654 -0.196 -0.001 0.008

MK8 24 4 0.750 0.613 -0.223 -0.002 0.012

MK3 24 4 0.625 0.676 0.075 -0.003 0.023

KWM12 24 6 0.583 0.690 0.155 -0.003 0.036

MK9 24 4 0.458 0.581 0.211 -0.004 0.047

MK5 25 3 0.680 0.666 -0.021 -0.001 0.011

Tur4_153 25 2 0.440 0.482 0.087 -0.002 0.015

Tur4_80 24 5 0.542 0.641 0.155 -0.004 0.038

Tur4_132 24 2 0.167 0.286 0.418 -0.006 0.101

Tur4_142 24 4 0.333 0.392 0.150 -0.004 0.043


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MH Tur4_162 23 5 0.652 0.733 0.111 -0.004 0.039

BB MK6 84 8 0.821 0.766 -0.072 0.000 0.001

Tur4_117 84 4 0.560 0.617 0.093 -0.001 0.005

Tur4_98 84 2 0.548 0.503 -0.089 -0.001 0.003

Tur4_66 84 2 0.036 0.035 -0.012 -0.002 0.004

E12 84 4 0.702 0.690 -0.018 -0.001 0.002

Tur4_108 84 2 0.083 0.080 -0.038 -0.003 0.006

Tur4_128 83 4 0.518 0.484 -0.071 -0.003 0.006

Tur4_111 84 3 0.464 0.403 -0.152 -0.002 0.003

Tur4_105 80 7 0.787 0.801 0.017 -0.003 0.006

D22 84 3 0.786 0.631 -0.245 -0.003 0.004

Tur4_87 84 3 0.548 0.506 -0.083 -0.004 0.005

Tur4_91 84 7 0.786 0.781 -0.006 -0.002 0.004

Tur4_138 84 5 0.702 0.746 0.059 -0.003 0.005

Tur4_141 84 8 0.774 0.772 -0.002 -0.002 0.003

F10 81 5 0.728 0.635 -0.147 -0.003 0.004

MK8 84 7 0.488 0.482 -0.013 -0.003 0.004

MK3 84 6 0.857 0.764 -0.121 -0.003 0.003

KWM12 84 7 0.667 0.699 0.046 -0.004 0.005

MK9 84 4 0.607 0.591 -0.028 -0.006 0.009

MK5 84 3 0.464 0.457 -0.015 -0.006 0.007

Tur4_153 84 2 0.464 0.472 0.016 -0.009 0.012

Tur4_80 84 5 0.548 0.583 0.060 -0.007 0.008

Tur4_132 83 2 0.458 0.471 0.028 -0.023 0.033

Tur4_142 83 4 0.554 0.521 -0.063 -0.012 0.016

Tur4_162 82 6 0.695 0.709 0.020 -0.010 0.014

BS MK6 21 6 0.810 0.692 -0.170 -0.001 0.002

Tur4_117 21 4 0.810 0.636 -0.273 -0.001 0.002

Tur4_98 21 2 0.476 0.417 -0.143 -0.001 0.005

Tur4_66 21 3 0.190 0.180 -0.060 -0.001 0.006

E12 20 4 0.750 0.674 -0.113 -0.001 0.009

Tur4_108 20 2 0.200 0.184 -0.086 -0.004 0.033

Tur4_128 20 3 0.600 0.591 -0.016 -0.002 0.016

Tur4_111 20 4 0.400 0.421 0.050 -0.003 0.028

Tur4_105 20 7 0.900 0.822 -0.094 -0.001 0.005

D22 21 3 0.762 0.570 -0.336 -0.001 0.003

Tur4_87 21 3 0.333 0.517 0.355 -0.003 0.018

Tur4_91 21 6 0.857 0.798 -0.075 -0.001 0.003

Tur4_138 21 5 0.762 0.770 0.011 -0.001 0.004

Tur4_141 21 8 0.857 0.844 -0.016 -0.001 0.003

F10 20 5 0.800 0.654 -0.223 -0.001 0.006

MK8 21 3 0.333 0.298 -0.120 -0.001 0.005

MK3 21 6 0.810 0.680 -0.191 0.000 0.002


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BS KWM12 21 7 0.762 0.724 -0.053 -0.001 0.003

MK9 21 3 0.429 0.531 0.193 -0.002 0.011

MK5 21 3 0.619 0.562 -0.102 -0.001 0.004

Tur4_153 21 2 0.333 0.498 0.330 -0.002 0.016

Tur4_80 21 2 0.476 0.417 -0.143 -0.001 0.005

Tur4_132 21 2 0.238 0.214 -0.111 -0.001 0.006

Tur4_142 21 4 0.381 0.392 0.027 -0.001 0.008

Tur4_162 21 6 0.810 0.758 -0.068 -0.001 0.003

AU MK6 29 9 0.759 0.784 0.033 -0.001 0.005

Tur4_117 29 3 0.586 0.584 -0.004 -0.001 0.007

Tur4_98 29 2 0.448 0.448 0.000 -0.002 0.010

Tur4_66 29 4 0.138 0.165 0.164 -0.002 0.012

E12 28 5 0.786 0.675 -0.164 -0.001 0.006

Tur4_108 29 2 0.345 0.334 -0.033 -0.001 0.009

Tur4_128 27 3 0.370 0.448 0.173 -0.005 0.058

Tur4_111 27 3 0.185 0.175 -0.057 -0.005 0.040

Tur4_105 25 7 0.720 0.812 0.113 -0.002 0.014

D22 29 3 0.586 0.517 -0.133 -0.001 0.007

Tur4_87 29 2 0.414 0.436 0.051 -0.002 0.011

Tur4_91 29 7 0.690 0.810 0.149 -0.001 0.012

Tur4_138 29 4 0.483 0.553 0.127 -0.002 0.011

Tur4_141 29 7 0.931 0.791 -0.178 0.000 0.002

F10 26 4 0.500 0.645 0.224 -0.006 0.065

MK8 29 6 0.724 0.666 -0.087 -0.001 0.005

MK3 28 6 0.571 0.636 0.102 -0.003 0.028

KWM12 29 5 0.655 0.692 0.053 -0.001 0.010

MK9 29 4 0.517 0.628 0.176 -0.002 0.013

MK5 29 3 0.724 0.602 -0.202 -0.001 0.004

Tur4_153 29 2 0.276 0.374 0.263 -0.002 0.019

Tur4_80 28 3 0.607 0.499 -0.218 -0.002 0.012

Tur4_132 27 2 0.185 0.171 -0.083 -0.005 0.046

Tur4_142 27 4 0.481 0.470 -0.024 -0.003 0.027

Tur4_162 27 4 0.630 0.674 0.066 -0.003 0.024

AL MK6 14 6 0.571 0.824 0.307 0.013 0.018

Tur4_117 14 3 0.714 0.588 -0.215 -0.001 0.007

Tur4_98 14 2 0.143 0.137 -0.040 -0.004 0.021

Tur4_66 14 3 0.500 0.418 -0.197 -0.005 0.014

E12 14 3 0.429 0.371 -0.156 -0.007 0.017

Tur4_108 14 2 0.357 0.302 -0.182 -0.012 0.024

Tur4_128 14 2 0.571 0.516 -0.106 -0.014 0.025

Tur4_111 14 3 0.357 0.319 -0.121 -0.014 0.025

Tur4_105 9 5 0.667 0.667 0.000 -0.034 0.053

D22 14 3 0.500 0.544 0.081 -0.022 0.035


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AL Tur4_87 14 3 0.286 0.409 0.302 -0.039 0.060

Tur4_91 14 7 0.857 0.802 -0.068 -0.013 0.018

Tur4_138 14 5 0.643 0.692 0.071 -0.030 0.040

Tur4_141 14 5 0.857 0.780 -0.099 -0.022 0.028

F10 11 4 0.727 0.714 -0.019 -0.077 0.097

MK8 14 4 0.786 0.604 -0.300 -0.030 0.037

MK3 14 3 0.429 0.585 0.268 -0.067 0.085

KWM12 14 4 0.786 0.626 -0.254 -0.038 0.044

MK9 14 4 0.571 0.563 -0.015 -0.041 0.050

MK5 14 4 0.857 0.640 -0.339 -0.034 0.040

Tur4_153 14 2 0.214 0.308 0.304 -0.109 0.135

Tur4_80 14 3 0.500 0.522 0.042 -0.082 0.096

Tur4_132 13 2 0.077 0.077 0.000 -0.282 0.395

Tur4_142 13 3 0.615 0.564 -0.091 -0.119 0.144

Tur4_162 12 5 0.833 0.742 -0.122 -0.075 0.085

ES MK6 9 7 0.667 0.889 0.250 -0.004 0.046

Tur4_117 10 4 0.900 0.744 -0.209 -0.001 0.006

Tur4_98 10 3 0.500 0.467 -0.071 -0.002 0.011

Tur4_66 10 2 0.100 0.100 0.000 -0.003 0.022

E12 9 5 0.556 0.785 0.292 -0.005 0.059

Tur4_108 10 2 0.300 0.267 -0.125 -0.002 0.014

Tur4_128 9 3 0.222 0.215 -0.032 -0.006 0.059

Tur4_111 9 2 0.333 0.292 -0.143 -0.005 0.048

Tur4_105 8 4 0.875 0.759 -0.153 -0.002 0.012

D22 10 3 0.400 0.489 0.182 -0.003 0.024

Tur4_87 10 2 0.600 0.522 -0.149 -0.002 0.012

Tur4_91 10 5 0.800 0.817 0.020 -0.001 0.008

Tur4_138 10 3 0.800 0.528 -0.516 -0.001 0.007

Tur4_141 10 4 0.600 0.594 -0.009 -0.002 0.011

F10 8 4 0.750 0.714 -0.050 -0.003 0.023

MK8 10 5 0.700 0.622 -0.125 -0.001 0.006

MK3 10 5 0.600 0.567 -0.059 -0.001 0.008

KWM12 10 6 0.600 0.772 0.223 -0.002 0.016

MK9 10 3 0.600 0.528 -0.137 -0.002 0.009

MK5 10 3 0.600 0.661 0.092 -0.002 0.012

Tur4_153 10 2 0.300 0.267 -0.125 -0.003 0.017

Tur4_80 10 4 0.700 0.722 0.031 -0.002 0.011

Tur4_132 10 2 0.100 0.100 0.000 -0.003 0.020

Tur4_142 10 3 0.600 0.617 0.027 -0.002 0.012

Tur4_162 8 6 1.000 0.804 -0.244 -0.001 0.007


167

Appendix A5.2. Bottleneck tests

Table A5.2.1. Summary statistics of the SMM (stepwise mutation model) and the

TPM (2-phased model) tests of bottleneck (Cornuet and Luikart 1996). Wilcoxon

test found significant heterozygosity excess (*) relative to numbers of alleles, which

are lost faster than heterogeneity in a bottleneck, after Bonferroni correction (Pcrit =

0.007).

Wilcoxon test (P-value)

Locality SMM TPM

PE 0.560 0.001*

MH 0.275 0.019*

BB 0.653 0.002*

BS 0.491 0.030*

AU 0.958 0.020*

AL 0.458 0.220*

ES 0.974 0.474*

Figure A5.2.1. Distribution of allele frequencies for each sampling locality of

bottlenose dolphins. PE = Perth (blue), MH = Mandurah (black), BB = Bunbury

(green), BS = Busselton (orange), AU = Augusta (purple), AL = Albany (grey), ES =

Esperance (pink).

0

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Appendix A5.3. Isolation by distance

Figure A5.3.1 Isolation by distance plot of correlation between untransformed

genetic differentiation (FST) vs. untransformed geographic distance (km) among all

sampling localities.

Mantel test with all sampling localities indicated a positive and significant

correlation between genetic and geographic distances (R = 0.58, P-value < 0.02).


169

Appendix A5.3. (ongoing)

Figure A5.3.2. Isolation by distance plot of correlation between untransformed

genetic differentiation (FST) and untransformed geographic distance (km) among

sampling localities (a) bordering the Indian Ocean (Swan Canning Riverpark (SCR),

Cockburn Sound (CS/OA), Mandurah (MH), Bunbury (BB) and Busselton (BS)) and

(b) those bordering the Great Australia Bight (Augusta (AU), Albany (AL) and

Esperance (ES))

(a) Mantel test between populations bordering the Indian Ocean (Swan Canning

Riverpark (SCR), Cockburn Sound (CS/OA), Mandurah (MH), Bunbury (BB) and

Busselton (BS)) indicated a positive and significant correlation between genetic and

geographic distances (R = 0.89, P-value < 0.01).

(b) Mantel test between populations bordering the Great Australia Bight

(Augusta (AU), Albany (AL) and Esperance (ES)) indicated a positive and

significant correlation between genetic and geographic distances (R = -0.93, P-value

= 0.84).


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Appendix A5.4. Mean of the posterior probabilities (LnP(D)) and ΔK statistic

for STRUCTURE

Figure A5.4.1. Mean of the estimated posterior probabilities (LnP(D)) and ΔK

statistic (Evanno et al. 2005) over ten replicate runs for values of K = 1-10 using the

Bayesian method in STRUCTURE and with (a and b) the full dataset (n = 221) or (c

and d) excluding samples from Bunbury (BB) locality (n = 136).

0

1

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3

4

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ΔK

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-13200

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Appendix A5.5. Correlation between sample size n and estimated effective

population size (Ne and cNe)

Figure A5.5.1. Correlation between sample size n and estimated sample size as

effective population size (Ne, black) or corrected effective population (cNe, red).

Black trendline (R = 0.8384, P-value < 0.05); red trendline (R = 0.8925, P-value =

0.05).

0

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60

80

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120

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160

0 20 40 60 80 100 120

Estim

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Appendix A5.6. Abundances estimated for dolphins using the Perth

metropolitan waters using single state Closed Robust Design models

Materials and Methods

For the purpose of this chapter, I run single state closed robust design (CRD) models

to obtain estimates of the seasonal abundances of dolphins. In this scenario (Scenario

3), the dataset was modified so all individuals appeared to be captured within the

same site.

In MARK, each CRD model combination was run with the probability of capture (p)

varying by primary period, and with recapture probability (c) set as equal to first

capture probability (p). The abundance (N) was set to vary by primary periods

[N(primary periods)]. Several sub-models for apparent survival (φ) were run (i.e.,

whether it varied by primary periods or none). Temporary emigrations and re-

immigrations (γ”, γ’) were also estimated whether it varied by primary periods or

none, or with other constraints such as: Random model with γ "k = γ'k; a Markovian

model where γ"k = γ"k-1 and γ' k = γ'k-1; or a mix of Random and Markovian models;

and a No movement model with γ"= γ'= 0.

Models were ranked using the Akaike Information Criterion (AICc, Burnham and

Anderson 2002). The model with most support by AICc (highest AICc weight) was

selected as the most parsimonious model. Models with ΔAICc < 2 were also

considered to have support from the data (Burnham and Anderson 2002).

Results

Models selection

With no stratification (single site, CRD model), two models best fitted the data and

attracted 74% of the AICc weight together. The first model described for constant

apparent survival, emigration, and re-immigration while the second one had

emigration varying by primary period (Table A5.6.1).


17

3

Table A5.6.1. Single state closed robust design models (in rank order of AICc scores). The table provides an overview of the Akaike Information

Criterion corrected for small sample size (AICc), difference in AICc with best-fitting model and AICc weight, the number of parameters used in

model fit and the deviance explained.


weight

Model


Scenario 3 (one states)

φ(.) γ"(.) γ'(.) p(period) N(period), p=c 1319.5 0.0 0.386 1.0000 35 6087.8

φ(.) γ"(period) γ'(.) p(period) N(period), p=c 1319.6 0.2 0.356 0.9235 49 6058.2

φ(.) γ"( period) γ'(period) p(period) N(period), p=c 1321.7 2.2 0.126 0.3265 61 6034.4

Random - φ(.) γ"(period) γ'(period) p(period) N(period), p=c 1322.2 2.7 0.099 0.2574 48 6062.9

φ(.) γ"(.) γ'(period) p(period) N(period), p=c 1324.5 5.1 0.031 0.0794 48 6065.3

Markovian - φ(period) γ"( period) γ'(.) p(period) N(period), p=c 1330.6 11.2 0.001 0.0038 62 6041.2

Note: φ apparent survival; γ" emigration; γ' re-immigration; p probability of capture; p = c probability of capture is equal to recapture; N

abundance; (.) constant; (period) varying by primary period. Some models have constraints: Random model in which γ "k = γ'k; Markovian

model where γ"k = γ"k-1 and γ' k = γ'k-1.


174

Abundance estimates

With a capture probability varying from 0.10 to 0.25 (mean = 0.17, SE 0.01), the

model yielded a constant apparent survival rate of 0.87 (SE 0.02, 95% CI 0.82-0.91).

The model also estimated an emigration rate of 0.09 (SE 0.02, 95% CI 0.05-0.14)

and a reimmigration rate of 0.70 (SE 0.09, 95% CI 0.51-0.84). Mean seasonal

abundance of dolphins using the study area was 153.8, SE 5.0 (95% CI 114.3-193.3)

and ranged from the lowest estimate of 94 (SE 16.8, 95% CI 66-133) in winter 2014

to the highest estimate of 224 (SE 37.9, 95% CI 162-312) in winter 2011, both

corrected for unmarked individuals (Figure A5.6.1).

Figure A5.6.1. Seasonal estimated abundances (Ntotal ± 95% confidence intervals)

of dolphins in Perth metropolitan waters (defined as one site).

0

50

100

150

200

250

300

350

Win

ter

Sp

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Su

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Autu

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2011 2012 2013 2014 2015

Estim

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(N

tota

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5%

CI)

Seasons

175

Chapter 6. General Conclusions

I have detailed a set of theoretical and practical methods that were applied in

research conducted for this thesis (and for associated projects in the case of the

southwestern WA genetics work) to assess the current status of bottlenose dolphins

residing in an estuary (the Swan Canning Riverpark - SCR), two embayments

(Cockburn Sound and Owen Anchorage – CS and OA, respectively) and an adjacent

area of open coastline (Gage Roads - GR) within Perth metropolitan waters.

This study was prompted by an unusual mortality event in the SCR estuary in 2009,

and the lack of understanding about the ecological and genetic connectivity between

previously defined resident subpopulations of bottlenose dolphins in Perth

metropolitan waters (Finn 2005; Chabanne et al. 2012).

Through the four data chapters, I have:

1. Provided estimates of abundance, apparent survival and demographic

movement between geographic regions within the Perth metropolitan waters

using a multistate modelling approach (Chapter 2, Chabanne et al. 2017b);

2. Combined information on social structure (i.e., social affinity and network),

home ranges, residency patterns and genetic relatedness to identify the

existence of local populations (i.e., ecological differences, Chapter 3,

Chabanne et al. 2017a);

3. Demonstrated that the relationship between socio-geographic structure and

genetic structure is not a straightforward relationship but a complex process

at temporal and spatial scales (Chapter 4); and

4. Assessed the dispersal along the coastline (i.e., broader regional-scale) that

the Perth metropolitan region belongs to and examined the efficacy of the

effective population size (Ne) and the ratio of effective/census population

sizes (Ne/Nc) as conservation tools (Chapter 5).

Taken together, the information in these four data chapters greatly improves the

scientific basis for decision-making on the conservation and management of

bottlenose dolphins in Perth metropolitan waters. I have indicated in each of the

Chapter 6 – General Conclusions

176

chapters, and will address again here, how this information provides valuable and

targeted guidance for management authorities and local industries to inform

management strategies with the aim of conserving not a single overall population of

T. aduncus in Perth metropolitan waters, but multiple distinct subpopulations

associated with particular locales. Below is a list of management strategies that will

be better informed and improved based on the information from this research:

Environmental Impact Assessment (EIA) of proposed coastal and estuarine

developments;

The design and construction-phase management of an "outer harbour" port in

CS (Western Australian Planning Commission 2004);

Formal management plans for the SCR and CS/OA (Department of

Environment 2003; BMT Oceania 2014);

Management actions for marine reserves (e.g., Shoalwater and Marmion

Marine Parks, Department of Conservation and Land Management 1992;

Department of Environment and Conservation 2007);

Species-specific management actions (e.g., feeding of dolphins in CS, anti-

disturbance or entanglement initiatives, Donaldson et al. 2012a).

In the following sections of this chapter, I summarise the key findings relevant to the

four subpopulations described in this thesis. I note that from this point on, however, I

will avoid using the term ‘subpopulation’ for the bottlenose dolphins associated with

GR (the open coastline area north of Fremantle) based on their ecological

characteristics (below). I then discuss some of the limitations I encountered during

the study and, specifically, whether they were practical in nature (e.g., relating to the

time and logistics of sampling) or related to the principles of the methods used in this

research. Finally, I provide specific management recommendations that should be

given due consideration by relevant management authorities and local industries.

6.1. Keys research findings for each subpopulation (Figure 6.1)

Considerable differences in population size and ecological parameters (i.e., social

affinity, home ranges) were found between the subpopulations inhabiting the

metropolitan waters of Perth, particularly between the estuarine subpopulation (SCR)

and the dolphins associated with the open coastline north of Fremantle (GR).


177

6.1.1. The Swan Canning Riverpark estuarine subpopulation

Both the MSCRD and social structure analyses (Chapters 2 and 3, Chabanne et al.

2017a, b) showed evidence of a stable, but small subpopulation of bottlenose

dolphins in the estuary (< 25 individuals). Their high apparent survival rate (0.98)

indicated an almost complete lack of permanent emigration during the study,

although the few movements detected in and out of the SCR generally involved

individuals from the estuary visiting the semi-enclosed embayments (CS/OA). The

inconsistency between those results and the occurrence of genetic dispersal in the

opposite direction (from OA to SCR, Chapter 4) refers to different timescales

involved: social is on a timescale less than one generation; assignment of individuals

to genetic clusters is on a one-generation timescale; and the genetic differentiation

indices (e.g., FST, sHUA) are on a multi-generation timescale.

Other characteristics included a high degree of social stability, few interactions with

individuals from adjacent subpopulations, individual home ranges almost exclusively

encompassed within the estuary, and year-round residency (i.e., occupancy

throughout all four seasons) and long-term site fidelity (i.e., several decades,

Chabanne et al. 2012). The Fremantle Harbour Port and other areas in the lower

reaches of the estuary were defined as hot spots (i.e., core areas representing 50% of

the kernel density estimation). All those characteristics were comparable to those

reported in a previous study (Chabanne et al. 2012) and suggest that the SCR

subpopulation may experience some degree of demographic isolation (Chapters 2

and 3, Chabanne et al. 2017a, b), which would make it vulnerable to long-term

population decline, possibly leading to local extinction of bottlenose dolphins in the

estuary. The small size of the subpopulation (and particularly the small number of

resident females) emphasises the potentially catastrophic effects of extreme

stochastic events, such as the unusual mass mortality event in 2009 (Holyoake et al.

2010).

The SCR subpopulation was not found to be genetically isolated, despite the higher

potential for inbreeding (Chapter 4) and higher genetic relatedness within that

subpopulation than within any of the other adjacent subpopulations (Chapter 3,

Chabanne et al. 2017a). Moreover, in the context of all the findings, the SCR


178

subpopulation may currently act as a sink within the Perth metropolitan

metapopulation and, thus, the SCR subpopulation would not likely survive without

the migration of individuals from another source, i.e., the adjacent subpopulations. In

the extreme scenario of an effective population size < 50, as was estimated for Perth

(Chapter 5), there is a plausible risk of local extinction (Crnokrak and Roff 1999;

Palstra and Ruzzante 2008).

6.1.2. The semi-enclosed embayment subpopulations: Owen Anchorage and

Cockburn Sound

Both the OA and CS subpopulations presented similar characteristics to each other

(Chapters 2 and 3, Chabanne et al. 2017a, b), with stable population sizes (c. 103

individuals combined; 43 and 63 individuals in OA and CS, respectively). Although

most individuals from both subpopulations showed a high degree of residency (i.e.,

year-round, with long-term site fidelity), the apparent survival was lower than 0.9

(Chapter 2, Chabanne et al. 2017b), suggesting that some individuals may move

permanently into adjacent waters further south, such as Shoalwater Bay and Warnbro

Sound (Green 2011) or perhaps west of Garden Island.

However, the OA and CS subpopulations were socially independent with limited

interactions occurring between subpopulations (Chapter 3, Chabanne et al. 2017a).

In addition, the home ranges of individuals within each subpopulation were mainly

limited to their respective geographic regions and, viewed cumulatively, indicated

distinct core areas of ranging that extended east and west of the shipping channel for

the OA subpopulation and within the Kwinana Shelf area for the CS subpopulation

(see Figure 3.5a, Chapter 3, Chabanne et al. 2017a).

While the weak genetic differentiation documented between the OA and CS

subpopulations (i.e., genetic input from adjacent subpopulation, Chapter 4) may

simply reflect the current social structure (Chapter 3, Chabanne et al. 2017a), it is

more conservative to treat each subpopulation as a distinct ‘unit to conserve’. A

historical genetic event (i.e., bottleneck event) was found for the CS subpopulation,

and my results indicated that contemporary gene flow occurs in one direction (from


179

OA to CS), suggesting a more vulnerable subpopulation (CS) and one (OA) that is

vital to conserve, for the sake of GR, CS and SCR subpopulations.

6.1.3. Bottlenose dolphins occurring along the open Gage Roads coastline

In contrast to the three other subpopulations, the ecological and genetic

characteristics of bottlenose dolphins observed along the open coastline north of

Fremantle do not readily support a conclusion that bottlenose dolphins in this region

constitute a ‘unit to conserve’. Such structure difference between open coastline and

estuarine systems is not unusual and has been reported elsewhere (e.g., California

coastline, Defran and Weller 1999; e.g., Texas coastline, Henderson 2004).

Nonetheless, the conservation of these dolphins may be critical (as a source

population) to the persistence of other subpopulations inhabiting the embayment and

estuary systems in Perth metropolitan waters.

Bottlenose dolphins in the GR area along the open coastline north of Fremantle were

abundant (the maximum population size estimated was c. 172 individuals), but there

was no obvious seasonal pattern occurrence and the majority of the individuals being

seen only once within the entire four-year study (Chapter 2, Chabanne et al. 2017b).

Although a fission-fusion society seemed to best describe the social structure of

dolphins in this area, their ecological characteristics principally indicated weak site

fidelity and more transient behaviour that that documented for the three

subpopulations to the south (Chapters 2 and 3, Chabanne et al. 2017a, b).

The small sample size of biopsies made it difficult to draw definitive conclusions

about the genetic characteristics of the bottlenose dolphins in this area (Chapter 4).

While my results suggested a lack of genetic differentiation from the other

subpopulations in Perth metropolitan waters, it is most likely that bottlenose

dolphins in this area form a larger population that extends further north. This

inference is supported by the core area of home ranges being located along the

northern edge of the study area (Chapter 3, Chabanne et al. 2017a) and, further, by

the findings from previous boat-based photo-identification surveys conducted

between 1991 and 1993 (Waples 1997).


180

6.1.4. Perth metropolitan waters dolphin population at a regional-scale

In my thesis, I also examined the genetic structure of bottlenose dolphins along the

southwestern coastline of Western Australia using microsatellite markers (Chapter 5)

and with a focus on a contemporary temporal scale. It appears that the Perth

population has been more isolated in the last few generations, with limited dispersal

from adjacent populations to the south. This result suggests a change in the

populations’ dynamic, as Manlik et al. (in prep-a) indicated that, historically, the

Bunbury population (located c. 180 km south of Perth) was the source population for

the southwestern coast, including the Perth metropolitan waters dolphin population.

The weak genetic structure and the source-sink dynamic found at a fine-scale

(Chapter 4) suggests that the Perth population might appropriately be described as a

metapopulation (cf. Wells and Richmond 1995). Such a conclusion, however,

requires further work, including research to: 1) identify the unknown ancestral

population found in admixed individuals and 2) assess the genetic connectivity with

populations located further north or offshore.

Effective population size estimates for Perth dolphins were not conclusive (Chapter

5). One estimate suggested some risk of local extinction (i.e., Ne < 50) while the

second was estimated in the same range as that of the Bunbury dolphin population

(i.e., Ne > 100), although the second estimate was obtained based on a larger sample

size and included individuals from the open coastline north of Fremantle (dataset

from Chapter 4).


18

1

Figure 6.1. Summary overview of the conservation status of bottlenose dolphins in the four geographic regions of Perth metropolitan waters:

SCR = Swan Canning Riverpark (red), CS = Cockburn Sound (yellow), OA = Owen Anchorage (yellow), GR = Gage Roads (green).


182

6.2. Theoretical and practical limitations of the study

All the methodological and analytical approaches applied in this thesis have inherent

limitations either practicability, in their assumptions, or available sample sizes.

However, their evaluation here in the context of this study should be of broader

interest for the future study, conservation and management of small cetaceans in

coastal and estuarine systems and, in some cases, for terrestrial and marine wildlife

generally.

6.2.1. Fine-scale delineation of subpopulations

The photo-identification surveys were designed according to the different habitats

(i.e., topography and bathymetry) identified prior to the study (i.e., the four

geographic regions). Using the data collected from those surveys, I used the

Multistate Closed Robust Design (MSCRD, Chapter 2, Chabanne et al. 2017b)

supplemented by social network and home range analyses (Chapter 3, Chabanne et

al. 2017a) to identify the presence of subpopulations of Indo-Pacific bottlenose

dolphins in Perth metropolitan waters.

While the social and home range analyses clearly suggested four subpopulations,

each associated with a different geographic region, the MSCRD approach did not

allow inference as to that structure because of a key assumption inherent to the

approach – namely, that individuals should not transit between geographic regions

during secondary occasions and within a primary period (Arnason 1972, 1973).

When considering the four geographic regions within the MSCRD models, 5% of the

captures violated this assumption, mostly because of individuals moving between CS

and OA. It was then more appropriate (theoretically) to pool data of both CS and OA

regions to minimise the violation of the transit assumption and to obtain less bias in

the estimates of survival rates and abundances (O'Connell-Goode et al. 2014).

6.2.2. Ecological characteristics of Gage Roads: are there sufficient data?

Mark-recapture, social structure and kernel density estimation (KDE) for home range

analyses are generally not suitable for highly mobile populations or populations with


183

a large number of transient individuals, mostly because of a lack of individual data

(i.e., insufficient sighting history data per individual). In my study, this issue arose

for dolphins sighted along the open GR coastline north of Fremantle. The estimates

of abundance were variable, with high variance in capture probabilities (Chapter 2,

Chabanne et al. 2017b). I was also unable to document their social structure with

clarity (Chapter 3, Chabanne et al. 2017a), because few individuals were sighted

more than five times (i.e., a threshold used to avoid potential bias from small

individual sample size, Jennions and Møller 2003).

In the context of this thesis, however, the results showed a marked difference in the

ecology of bottlenose dolphins in that locale compared to those in the other study

regions. In future, more capture-recapture data are required to obtain more reliable

estimate of abundance and other demographic characteristics for dolphins in the

open coast waters.

6.2.3. Genetic sampling is not random

Obtaining tissue samples from free-ranging and highly mobile species that spend

much of their time underwater is challenging. In most cases, genetic sampling is

conducted during systematic and randomised surveys (e.g., Ansmann et al. 2012).

However, the samples themselves are not necessarily randomly collected. For

bottlenose dolphins, genetic sampling often targets individuals that can be easily

identified (i.e., those with well-marked dorsal fins), as well as those with some

photo-identification history (i.e., multiple captures). Time and cost also contribute to

the ‘non-random nature of biopsy’ sampling, with multiple samples often taken from

the same area or social group, if previous attempts have not disturbed the dolphins.

This applies to how genetic sampling was conducted in my study and explains a lack

of samples from bottlenose dolphins sighted along the GR coastline north of

Fremantle. In some instances, behavioural heterogeneity between age classes and

sexes, and perhaps prior exposure to boating activity, may contribute to non-random

biopsy sampling (e.g., Quérouil et al. 2010).

The consequences of the non-random nature of the genetic sampling process are

numerous and include (but are not limited to): 1) possible inaccurate representation


184

of the true sex ratio; 2) limited sex-specific interpretations (e.g., abundances,

survival rate); and 3) a lack of power in estimating parameters and defining patterns

(e.g., genetic structure, differentiation FST).

6.2.4. Contemporary effective population size (Ne) and ratio of effective/census

population size (Ne/Nc): unresolved conservation tools

Theoretically, the contemporary effective population size (Ne) is an important

variable that needs to be estimated to properly evaluate the ecology and evolution of

natural populations and, thus, to inform conservation planning and management with

smaller Ne values indicating faster loss of genetic variation upon which adaptation

depends (Frankham et al. 2010). Similarly, the ratio of effective/census population

sizes (Ne/Nc) can be a valuable tool for wildlife conservation efforts at the

population-level, especially when data collection may be difficult to undertake

(molecular or mark-recapture). In this research, I aimed to estimate Ne and assess the

extinction risk of the overall Perth metropolitan waters dolphin population. I also

aimed to estimate the Ne/Nc ratio to investigate the census population size (Nc) of

other populations along the southwestern coastline of WA that are not well-studied.

Several points can be made around the lack of success in achieving these objectives.

First, the results clearly indicated a high correlation between the sample size (n) and

the estimates of Ne for each population, suggesting that Ne was not successfully

estimated as the numbers of breeders in a population but, rather, simply another

sample size estimate. Therefore, estimates of any parameter associated with Ne (e.g.,

Ne/Nc ratio and estimation of Nc via the ratio) are currently inaccurate because of the

sample size correlation.

Second, Ne and the Ne/Nc ratio remain challenging to determine for long-lived and

highly mobile animals, despite efforts to correct the estimates for overlapping

generations (Ansmann et al. 2013; Brown et al. 2014; Waples et al. 2014) and to

define biases due to migration (Waples and England 2011).


185

Third, a large number of molecular samples for any putative population are required

in order to obtain reliable estimates and I only had a small number of samples

available.

Finally, there are few comparable studies that might assist in evaluating the

appropriateness of the parameters used in this study. More work is therefore required

in the estimates of Ne and the Ne/Nc ratio to properly interpret and use them as

indicators for contemporary conservation planning and management.

6.3. Management recommendations

In my research, I found that all subpopulations around Perth were connected, as in a

metapopulation (Wells and Richmond 1995). This is an important dynamic for the

long-term persistence of the subpopulations (Wiens 1976), particularly the SCR

subpopulation which experienced an unusual mortality event in 2009 (Stephens et al.

2014). It is, therefore, important to minimise anthropogenic activities that would

cause habitat fragmentation or otherwise interfere with dolphin movement patterns

within the region’s coastal and estuarine matrix.

However, it is also important to monitor each subpopulation as a distinct ‘unit to

conserve’, given evidence of variation in ecological and demographic parameters

and thus, potentially, differing pressures from local stressors and mortality factors.

Additionally, subpopulations represent a functioning ecological component of the

environments they are associated with and support economic (e.g., tourism) and

cultural values (i.e., dolphins have a high conservation value throughout the Perth

metropolitan area because of their iconic status and high public profile). The decline

or local extinction of bottlenose dolphins would mean that: i) few (or no) dolphins

would be present to maintain the ecological function of dolphins in those

ecosystems; and ii) tourism industries and amenity or aesthetic values that depend on

the presence of dolphins would be jeopardised.

Therefore, over the long-term, there is a justifiable reasoning that:


186

1) Each subpopulation should be considered as a ‘unit to conserve’. The lack of

fine-scale genetic population structure should not lead to the conclusion that no

population structure exists. A source-sink dynamic system may explain such

results, in which case management strategies should ensure the protection of the

source subpopulation. Additionally, any ecological differences between

subpopulations must be considered and, in some circumstances, may be as

informative as genetic differences (e.g., Taylor 2005).

2) High use areas for dolphins should be considered for the highest level of

protection. Core areas have ecological significance for dolphins as foraging

habitat, nursery area (Finn 2005; Finn and Calver 2008; Chabanne et al. 2012),

breeding (e.g., OA) and passage (i.e., transit) areas (Chapter 3, Chabanne et al.

2017a). In Chapter 3 (Chabanne et al. 2017a), I examined the risks of direct and

indirect threats caused by human activities on bottlenose dolphins. If

anthropogenic impacts occur in core areas, it may mean that a subpopulation

becomes demographically isolated (e.g., SCR subpopulation), or that

ecologically vital processes (i.e., foraging, nursing) are repeatedly disturbed,

resulting in negative effects on the viability of the subpopulation.

3) The small resident subpopulation in the Swan Canning Riverpark needs

additional protection, a recommendation supported by research conducted for

this thesis indicating its ‘at risk’ status based on subpopulation characteristics

(Figure 6.1) and other recent research on the subpopulation (Marley et al. 2016).

Marley et al. (2016) indicated that, despite being frequently exposed to vessel

traffic, bottlenose dolphins show varying levels of tolerance to acoustic

disturbance associated with particular areas in the Swan Canning Riverpark.

Marine mammals are already protected under Australia’s EPBC Act, but more

formal protection may be required. By way of example, the Adelaide Dolphin

Sanctuary was established in 2008 in South Australia to conserve bottlenose

dolphins facing similar threats as those that occur in the SCR, e.g., entanglements

in fishing gear, vessel strikes, and pollution (Department of Environment and

Heritage 2008; Steiner and Bossley 2008). The sanctuary, in association with

education efforts, has proved to be effective as abundance of dolphins within the


187

sanctuary is increasing (Bossley et al. 2017). Implementation of similar formal

protection would help integrate management efforts across stakeholder groups

and focus community support on protecting dolphins and their habitat.

4) The semi-enclosed embayment (counting Owen Anchorage and Cockburn Sound

as a single system), which includes both the OA and CS dolphin subpopulations,

is also important based on sighting frequency, residency and long-term site

fidelity and potential impacts on dolphins should be adequately considered in

Environmental Impact Assessment for any development proposal. In particular,

OA subpopulation acts as a source for all the others subpopulation s. It is

therefore critical to protect this subpopulation for the survival of other

subpopulations.

Although all of the above can lead to a view of looking at potential disturbances and

how changes in those could enhance habitat and potentially increase the population

size, consistent on-going monitoring of the subpopulations and their environmental

conditions is necessary for their effective conservation (Reeves et al. 2003;

Hammond 2010). Moreover, it is important that local management agencies, NGOs,

industries and other stakeholders become involved in the development of

conservation processes. Educational programs that enhance public awareness about

human impacts on dolphins and the marine environment in general are also

advisable. Within the last decade, some significant education efforts have been

implemented in Perth, notably the Dolphin Watch project (Government of Western

Australia 2017). Dolphin Watch is a community-based engagement program that

aims to:

Educate the public about the bottlenose dolphins in the Swan Canning Riverpark.

New volunteers are provided with: training that informs how an estuary system

functions and how human activities can impact the system; updates on the

ecology of the resident subpopulation of dolphins; examples of the impacts of

discarded fishing line and vessel noise and collisions on dolphins; and guidance

on safe interactions with dolphins.

Engage citizens in the conservation of the resident subpopulation of dolphins in

the Swan Canning Riverpark by collecting data on dolphin sightings (i.e., citizen


188

science); recognising individuals observed through ‘Finbook’ (i.e., a catalogue of

dorsal fins updated every year: Department of Parks and Wildlife 2016); and

contributing sightings/observations and reporting any incidents or abnormal

behaviour.

In addition to the recommendations made above, in the course of the chapters I have

indicated how coastal and estuarine developments could impact the subpopulations

within the metropolitan waters of Perth and the different risk level expected for each

subpopulation. Figure 6.1 can assist managers and stakeholders (e.g., Department of

Parks and Wildlife, Council of Cockburn Sound, and Fremantle Harbour Ports) in

visualizing the status of bottlenose dolphins in the different geographic regions of

Perth metropolitan waters in assessing the potential impacts of proposed

developments and where management strategies are most needed.

6.4. Concluding remarks

This research improves the scientific basis for environmental decision-making

relating to dolphins in the Perth metropolitan area. Critically, the research will

inform management decisions for the different subpopulations present, all of which

have different ecological and genetic characteristics.

It is therefore essential that an effective management strategy is developed to

mitigate the effects of human activities on the bottlenose dolphin subpopulation

associated with each geographic region of Perth metropolitan waters. In particular, it

is critical to maintain a stable and healthy OA subpopulation as it acts as the source

subpopulation to all the other subpopulations in Perth metropolitan waters.

Additionally, the resident subpopulation of bottlenose dolphins within the Swan

Canning Riverpark presents all the characteristics of being ‘at risk’: small population

size, connection with the adjacent subpopulations upon which their persistence

depends (i.e., genetic flow), and habitat that is subject to heavy industrial and

recreational use for human.

In this thesis, I used diverse methodological approaches, including field data

collection and analytical techniques, to study dolphin ecology, behaviour and genetic


189

characteristics, which led to the identification of multiple subpopulations in a

heterogeneous study area. I recommend that more studies consider such an approach

to better inform management and mitigate anthropogenic activities at appropriate

spatial scales. Indeed, the multi-strategy approach used in this thesis is broadly

applicable to any individually identifiable marine and terrestrial wildlife species.

191

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