Southern Cross University Daniele Cagnazzi Southern Cross University Theses Publication details

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1 Southern Cross University Theses 2010 Conservation status of Australian snubfin dolphin, Orcaella heinsohni, and Indo-Pacific humpback dolphin, Sousa chinensis, in the Capricorn Coast, Central Queensland, Australia Daniele Cagnazzi Southern Cross University Publication details Cagnazzi, D 2010, 'Conservation status of Australian snubfin dolphin, Orcaella heinsohni, and Indo-Pacific humpback dolphin, Sousa chinensis, in the Capricorn Coast, Central Queensland, Australia', PhD thesis, Southern Cross University, Lismore, NSW. Copyright D Cagnazzi 2010 epublications@scu is an electronic repository administered by Southern Cross University Library. Its goal is to capture and preserve the intellectual output of Southern Cross University authors and researchers, and to increase visibility and impact through open access to researchers around the world. For further information please contact epubs@scu.edu.au.

2 Conservation Status of Australian snubfin dolphin, Orcaella heinsohni, and Indo-Pacific humpback dolphin, Sousa chinensis, in the Capricorn Coast, Central Queensland, Australia Thesis submitted by Daniele Cagnazzi July 2010 for the degree of Doctor of Philosophy School of Environment and Management Southern Cross University Lismore Australia

3 Thesis declaration I certify that the work presented in this thesis is, to the best of my knowledge and belief original, except as acknowledged in the text, and that the material has not been submitted, either in whole or in part, for a degree at this or any other University. I acknowledge that I have read and understood the University s rules, requirements, procedures and policy relating to my higher degree research award and to my thesis. I certify that I have complied with the rules, requirements, procedures and policy of the University (as they may be from time to time). Print Name:... Signature:.. Date:.. II

4 Abstract This study examined populations of Australian snubfin dolphins (Orcaella heinsohni) and Indo-Pacific humpback dolphins (Sousa chinensis) inhabiting the coastal waters of the Capricorn Section of the Great Barrier Reef Marine Park, Queensland, Australia. A total of 189 humpback dolphins and 54 snubfin dolphins were identified during 1,760 hours and 20,248 kms of transect surveys completed between 2006 and 2008 in the Capricorn Coast region. Humpback dolphins were found in three different sites: Port Curtis, Keppel Bay and the Northern Region of the study area. In contrast, snubfin dolphins were found only in the Keppel Bay study area. Both humpback and snubfin dolphins were found year round with no significant variation among sampling periods and between seasons. Similarly, no substantial variation was found in pod size and composition among sampling periods and seasons. Based on the distribution of resighted individuals and the pattern of associations, it was established that humpback dolphins in the Capricorn Coast were grouped into three largely geographically distinct communities, referred to as the Port Curtis, Keppel Bay and the Northern Region communities. The interaction recorded among communities was lower than the level of association within communities. However, interaction among some members of different communities and movement of individuals between sites was recorded. Analysis of molecular variance showed high gene flow among Port Curtis and Keppel Bay study sites, which corroborates results from photo-identification data and social structure analysis. Very low gene flow was found between humpback dolphin populations from the Capricorn Coast and the Great Sandy Strait, which suggests that humpback dolphins from the Capricorn Coast form a separate management unit, which is geographically and mostly genetically isolated from populations elsewhere. Interestingly, migration rates for humpback dolphins between the Northern and Southern Great Sandy Strait was low, and significant differences in microsatellite loci frequency were evident between these communities. Abundance of humpback dolphins was estimated for the entire population and by geographical area using open population models. Estimates for Port Curtis, Keppel Bay and the Northern Region indicate that about 85 (PC = 85.1, SE = 4.36, 95% CI: ), 107 (KB = , SE = 4.87, 95% CI: ) and 64 (NR = 64.1, SE = 4.82, 95% CI: ) humpback dolphins used those areas respectively during the study. The total III

5 humpback dolphin population size in the Capricorn Coast region was estimated to be 256 (N tot = 75.80, SE = 8.12, 95% CI = ), of which 178 (N ma = 178.1, SE = 4.14, 95% CI: ) were adults. In contrast, snubfin dolphins were found only in Keppel Bay, and social structure analysis did not provide any evidence of structure in the population. This population appears to be geographically and demographically isolated. Genetic evidence based on a small sample size suggests that this population is genetically isolated from populations in North Queensland. A review of sighting information indicates that the Keppel Bay snubfin dolphin population is the southernmost population of snubfin dolphins along the Queensland coast. Population estimates indicate that about 74 snubfin dolphins (SD = 74.03, SE = 4.14, 95% CI: ) live year round in Keppel Bay. Statement on the contribution of others Project support Supervision Fitzroy Basin Association $128,000 SCU postgraduate budget $1,500 Burnett and Mary Regional $28,000 Group Professor Peter Harrison Editorial assistance Assistance in genetic analyses Professor Peter Harrison Dr. Michael Kruetzen IV

6 Acknowledgements For most of the kids born in the cold foggy town of Lodi, just 20 km southeast of Milan, in the middle of the Padana flat, in the North of Italy, the future is working in one of the several industries that dominate that region. Fortunately, that is not my present work neither it will be my future. For this I can only thank the two most important people of my life, my parents, or like we say in Italy mio papà e mia mamma. Thanks to them I had the opportunity to travel the world, to see beautiful places and to fall deeply in love with the ocean and its inhabitants. When I was 14 I dived for the first time, and since then my life had a single purpose, becoming a marine biologist. I also wanted to become a tennis champion but that never really worked out. Through high school and University my parents always supported my choices, they never put obstacles in my way, even if they knew that my dreams would have brought me to faraway countries, probably somewhere across the ocean. I don t know yet if I can consider myself a marine biologist but I am close enough to consider my dream fulfilled. I will never being able to thank mia mamma e mio papà enough. It is commonly believed that the best gift for a parent is the happiness of their kid, well I don t know if that is true, but if it is: mamma, papà, I am happy, I am living my dream and only thanks to you. This was my journey toward the world of marine biology, my parents gave me the opportunity to chose, an opportunity often underestimated, while my supervisor, Professor Peter Harrison gave me the opportunity to live my dream. Through my Master degree and my PhD you have been more than just a supervisor. You have been next to me in all the moments of my PhD, positive and negative but also in difficult moments of my life like after the car accident. Your guidance, patience, and confidence in my work, in particular when I didn t have it, made a great difference during the several up and down moments of PhD student life. I hope your efforts will be rewarded and that you are and will be proud of my results. This project was possible thanks to the support of the Fitzroy River Basin Association and the Burnett and Mary Regional Group that supported me for the length of the project both financially and logistically. I want to thank in particular Shane Westley, Coastal and Marine Coordinator at the FBA and Sue Sargent, Coastal Marine Coordinator for the BMRG. It has been a great pleasure to work with both of you, thanks for the support and the interest that you both showed in my project. I also thank the Great Barrier Reef Marine Park Authority V

7 and the Queensland Parks and Wildlife Service for providing the permits to carry out my fieldwork. I could not have carried out this project without the help of my assistants and volunteers. I want to thank all of you for your time, souls and hearts given to this project. Special thanks (in no particular order) to: Anne Sleeman, Alice Soccodato, Monica Mariani, Jessica Morris, Natalie Bush, Wayne Ships, Marjoleine Merel Hansje Ross, Monica Soldà, Francesca Di Dio, Yui Shibata, Kate Sprongis, Irene Pizzeghella, Simona Turbian, Robin Remko, Samantha Jo Hale and Maxine Bogart. Thank to all of you for the many hours watching just water, water and water until: 2DOLPHINNNNNs at o clock and thanks for the many boring days sitting and waiting for the SE winds to drop. I hope in the future I will be able to help all of you as much as you helped me in the last four years. I would also like to thank some of my colleagues for their very useful advice and fundamental support toward the project. I have to start with Guido Parra, with whom I shared my frustrations in particular after a bad sampling day. Michael Krützen, thank for the opportunity to be part of the EGGs and to work in your laboratory at the Anthropological Institute and Museum, University of Zurich. I had a wonderful time and I think I wouldn t be the same researcher without that opportunity. While I was there I also had the pleasure to meet great people and scientists: Anna, Alex, Corinne, Natasha, Nadja, Pirmin and Josephine. I would never have expected so much help, from people that hardly knew me. I felt like one of you from the beginning. You never made me feel like an intruder. You made my journey into genetics way easier. Special thanks go to Luciana Moller and Kerstin Bilgmann who trained me in biopsy sampling techniques. In this journey I met many great people that will be part of my life as a scientist but also as friends, Lars, Simon, Dee, Alex, and again Guido and Michael. I will remember the trip in Western Australia forever. Thanks for the long chats, discussions, advice, and laughs during the most difficult moment in my PhD career. I am looking forward to spending more time in the field with all of you. Particular thanks go to Kerrie Stimpson, simply, the greatest friend I could have found. My final thanks go to one particular person. I am not going to write the name of this person and why this person is so important to me, sometime there are just no words, sometime words are just too limited, so just thank you. I met lots of people through this long journey and listing all of them would be reductive, so to all of you, I just want you to know that even if your name is not on this thesis, it is written in my heart and thoughts. VI

8 This is the end of an incredible journey, a long journey full of obstacles but rich with incredible experiences. This is a part of my life that I will never forget even if now I just want to close it and I can finally write the word THE END. VII

9 Table of Contents Chapter 1 Introduction Introduction Rationale for this research Introduction to Management Units Research aims and objectives Structure of the Thesis... 6 Chapter 2 Summary of current knowledge: Australian snubfin dolphin and Indo- Pacific humpback dolphin Summary of current knowledge: Australian snubfin dolphin and Indo-Pacific humpback dolphin Taxonomy External morphology and life history of snubfin and humpback dolphins in Australian waters Distribution of Australian snubfin and humpback dolphins Habitat Abundance Social organisation, site fidelity and movement Conservation status and threats Conservation status in Australia Habitat degradation and loss Overfishing By-catch Pollution Vessel Traffic Chapter 3 Distribution and school dynamics of humpback dolphins and Australian snubfin dolphins in the Capricorn Coast region Introduction Materials and methods VIII

10 3.2.1 Study area Survey design Data collection and definitions Data analysis Results Summary of humpback and snubfin dolphin distribution patterns from the first study period: January-September Overall survey effort Sighting rates of humpback and snubfin dolphins School size and composition Discussion Survey limitations and related issues Humpback and snubfin dolphin distribution Sighting rate, school size and composition Chapter 4 Social structure, home range and habitat preference of Australian snubfin and humpback dolphins in the Capricorn Coast region Introduction Materials and Methods Data collection Identifying a network of communities in the population Home range Habitat use Results Survey effort Social structure of humpback and snubfin dolphins Home range Habitat selection Distance analysis, distribution of dolphins in relation to land, intertidal habitats and deeper waters Discussion IX

11 4.4.1 Limitations Social and spatial structure Chapter 5 Population genetics of Australian snubfin and humpback dolphins Introduction Conservation genetics and importance to conservation biology Genetic approach to identify management units Conservation in cetaceans Data Collection Procedures for sample collection Material and methods: DNA extraction and amplification DNA purification and extraction Genetic sexing Mitochondrial DNA screening and sequencing Microsatellite genotyping Data analysis MtDNA data analysis Microsatellite data analysis Results Humpback dolphin population structure Population genetic structure Snubfin dolphin population analysis Discussion Data consideration Within population levels of genetic diversity Genetic differentiation and migration rate among humpback dolphin populations Genetic isolation of Keppel Bay snubfin dolphins Chapter 6 Population estimates of Indo-Pacific humpback dolphins in the Capricorn Coast region Introduction X

12 6.2 Methods Survey procedures and data collection Data selection Estimating population size Population estimates using open population models Results Photo-Identification data Model selection Validation of model assumption Population estimates of humpback dolphins Discussion Chapter 7. Population estimates of Australian snubfin dolphins in the Capricorn Coast region Introduction Methods Survey procedures, data collection and effort Photo-identification, data collection and selection Validation of mark recapture assumptions Closed versus open population models Population estimates Total population size and population size of mature individuals Population estimates using POPAN open population model Results Photo-identification data Model selection Population estimates Discussion Geographic isolation Population size XI

13 7.4.3 Distribution Chapter 8. General Discussion Introduction Summary of major results from this study Survey limitations and related issues Humpback dolphins: summary of results Australian snubfin dolphins: summary of results Conservation status of humpback and snubfin dolphins in the Capricorn Coast Overview of IUCN list criteria and categories for the assessment of regional sub-populations Classification of the Capricorn Coast humpback dolphin sub-population under the IUCN B and D categories Classification of snubfin dolphins under the IUCN B and D categories Future research Final remarks References Appendices XII

14 List of Figures Figure 2.1 Australian snubfin dolphin, Orcaella heinsohni, from the Fitzroy River, Queensland Figure 2.2 Indo-Pacific humpback dolphin, Sousa chinensis, from the Fitzroy River, Queensland Australia Figure 3.1 Map of the Capricorn Coast study area. The red line represents the 30 m contour depth that was generally used as the offshore limit Figure 3.2 Port Curtis study area with the general route ( ) followed during boat based surveys done between 2006 and Red circles indicate sightings of humpback dolphins between Jan.-Sept. 2006, while green circles are humpback dolphin sightings recorded between October-September The green flag represent the boat ramp used during the study Figure 3.3 Map of the Keppel Bay study area showing the four transects followed during surveys done between January and September Humpback dolphin sightings observed during the same period are represented with red triangles, while snubfin dolphins are represented with blue circles. The Coorio Bay transect is shown with a black line with two consecutive perpendicular segments ( ), the Fitzroy estuary transect is shown by a black line with one perpendicular segment ( ), the Keppel Island transect by a continuous black line ( ), and the Fitzroy River transect by a series of lines and dots ( ). Green flags represent boat ramps used during the study Figure 3.4 Map of the Keppel Bay study area including transects surveyed during the second and third sampling periods, between October 2006 and September 2008, showing sightings of snubfin dolphins (yellow circles) and humpback dolphins (red triangles). Green flags indicate departure points. The Coorio Bay transect is shown by a series of lines and dots ( ), the Fitzroy estuary transect is shown by a black line with one perpendicular segment ( ), and the Fitzroy River transect was left unvaried from the first sampling period. Green flags represent boat ramps used during the study Figure 3.5 Transect survey lines designed using DISTANCE, that were followed to conduct unbiased preliminary surveys in the Northern Region (a) and Shoalwater Bay (b), between January and September Figure 3.6 Map of the Northern Region of the study site, showing the survey route followed during the second and third study periods ( ) (October 2006 and to September 2008), the 30 m contour line used as the offshore limit ( ), and humpback dolphin sightings recorded during the first study period (January and September 2006) ( ) and the second and third study periods ( ) XIII

15 Figure 3.7 Map of the Nine Mile Beach transition area showing the transect line ( ) sightings of bottlenose dolphins (Tursiops spp.) ( ) and humpback dolphins ( ) observed over the entire study period. The 30 m contour depth line was used as the offshore survey limit Figure 3.8 Map of the East Curtis Island Coast transition area showing the transect line ( ) sightings of offshore and inshore bottlenose dolphins (Tursiops spp.) (green triangle) and the only record of humpback dolphins (red square) and snubfin dolphin (blue circle) over the three years of the study. The 20 m contour depth line was used as the offshore survey limit the research boat was not allowed to go any further offshore under the Marine Safety Queensland boating regulations. The boat ramp used during the study is indicated with a green flag Figure 3.9 Number of surveys completed in Shoalwater Bay (SHB), Northern Region (NR), Keppel Bay (KB) and Port Curtis (PC), pooled for summer months (October-April) and winter months (May-September Figure 3.10 Survey efforts in hours (bars) completed per month and years in Keppel Bay (a), Port Curtis (b) and Northern Region (c) plotted together with the mean wind speed estimated at 9.00 am (Wind 1: black circles) and 3.00 pm (Wind 2: white squares). A Beaufort sea state of 1 requires a wind speed to be below 15 km/hr Figure 3.11 Boxplots indicating: (a) yearly variation in snubfin (OH) and humpback dolphins (identified with the region code GL, KB, PC) sighting rates, (b) school size and composition, (c) number of adults, (d) juvenile, and (e) calves observed per school. Data were pooled per survey period here with the year in which the major numbers of survey were completed, 06 = first survey period, 07 = second survey period, 08 = third survey period Figure 3.12 Relative frequency distribution of school size and school composition of snubfin dolphins in Keppel Bay (KB OH), and humpback dolphins in Keppel Bay (KB SC), Port Curtis (PC SC) and Northern Regions (PC SC) Figure 4.1. Dendrogram showing average linkage cluster analysis of 84 humpback dolphins using mean sighting locations and HWI values. Individual dolphins are represented by codes based on the preliminary community classification on the left of the figure. Clusters of dolphins are joined by vertical lines. The different colours indicate the three clusters or communities: Keppel Bay (red), Port Curtis (green) and Northern Region (Blue) Figure 4.2. Dendrogram showing average linkage cluster analysis of 44 snubfin dolphins using mean sighting locations and HWI values. Individual dolphins are represented by identification numbers. Clusters of dolphins are joined by vertical lines Figure 4.3 The social network of the humpback dolphins population presented by applying the Girvan Newman algorithm on association data after the cut-off permutation technique. Dolphin identification codes are shown in white squares with expected community XIV

16 membership and identification number. Three communities are evident: Northern Region (yellow), Keppel Bay (blue) and Port Curtis (red) Figure 4.4.The social network of the snubfin dolphins population presented by applying the Girvan Newman algorithm on association data after the cut-off permutation technique. Dolphin identification codes are shown in white squares with their identification number Figure 4.5. Representative ranges (95% kernel range) for the three humpback dolphin communities distinguished by association patterns in the Capricorn Coast region Figure 4.6. Representative ranges (95% kernel range) for snubfin dolphins in the Capricorn Coast region Figure 4.7 Map of Keppel Bay showing the different habitat types defined based on water depth, and including sightings of humpback dolphins (red circles) used in the analysis of habitat preference and distance analysis Figure 4.8 Map of Port Curtis showing the different habitat types defined based on water depth, and including sightings of humpback dolphins (red circles) used in the analysis of habitat preference and distance analysis Figure 4.9 Map of Keppel Bay showing the different habitat types defined based on water depth, and including sighting of snubfin dolphins (red circles) used in the analysis of habitat preference and distance analysis Figure 5.1. Geographic distribution of the 71 biopsy samples of humpback dolphins collected between 2008 and Figure 5.2. Geographic distribution (red areas) of the 41 samples of snubfin dolphins used in the analysis Figure 5.3. Picture taken during a successful attempt showing the dart in red near the top of the picture, just before it hits the dolphin Figure 5.4 Structure plots showing estimated proportions of the coefficient of admixture of each individual s genome that originated from K population, for K = 3 (a) and k = 2 (b) without prior information on dolphin location. Each individual is represented by a column. Geographical origin of the samples is given below the graphic. The numbers 1 4 indicate the sampling location being KB, PC, NGSS and SGSS respectively Figure 5.5 Structure plot of the estimated proportions of the coefficient of admixture of each individual s genome that originated from K = 2 populations, with prior information on dolphin location. Each individual is represented by a column. Geographical origin of the samples is given below the graphic. The numbers 1 4 indicate the sampling location being Halifax Bay, Cleveland Bay, Hinchbrook Channel and Keppel Bay, respectively Figure 6.1. Photographs of humpback dolphins of different age-class. In these photographs, it is possible to discern the colour variation and difference in size used to distinguish among a) juveniles, b) young adults and c) older adults (from left to right) XV

17 Figure 6.2. Number of seasons (dark gray) and years (light gray) in which, the 21 humpback dolphins sighted in more than one core study area, were sighted in each area. Each of the 21 dolphins shows a stronger preference to a particular site, while only one record was obtained in a different area. On the horizontal axis PC, KB and NR defined the core study area in which a particular individual, identified with the catalogue number, was sighted Figure 6.3. Resighting pattern of humpback dolphins. In KB and PC sighting patterns of humpback dolphins are summarised by showing the number of dolphins (y axis) sighted in different combinations of capture occasions (at the maximum 4) and sampling periods (at the maximum 2) (x axis). In the NR only the number of years in which dolphins were sighted was used (z) Figure 6.4. Discovery curves of the cumulative number of adult humpback dolphins (adults) and adults + juveniles humpback dolphins (all) identified between January 2006 and October 2008 in Keppel Bay (top), and Port Curtis (bottom) Figure 6.5. Discovery curves of the cumulative number of humpback dolphins identified between January 2006 and October 2008 in the Northern Region Figure 7.1 Discovery curve showing the cumulative number of snubfin dolphins (grey line) identified in relation to hours of survey per month (histograms) in Keppel Bay between 2006 to Figure 7.2. Sighting frequency for the 54 snubfin dolphins identified in the Keppel Bay core areas Figure 7.3 The 95% distribution range of snubfin dolphins is concentrated in the Fitzroy River area. A few schools were also observed in proximity of Coorio Bay, and only one school was observed north of Coorio Bay. A single dolphin was also sighted in proximity of Cape Capricorn but this was likely to represent an individual outside of the normal range. 147 XVI

18 List of Tables Table 3.1 Summary of the expected and completed survey effort in the Northern Regions and Shoalwater Bay during the first sampling period: column 1) A transect angle, 2) N number of transects per sub-areas, 3) T.L. total transects length 4) T.C. total length including movement between transects, 5) A.T. total area of the substrata, 6) A.C. total area surveyed, 7) P proportion of area surveyed, 8) R number of replicates per sub-areas, 9) Hrs hours of survey, columns 10-12) h, s and b = total number of humpback, snubfin and bottlenose dolphin schools sighted in the Northern Region and Hervey Bay during line transect surveys completed in the first sampling period Table 3.2 Mean hourly sighting rate per core areas and within areas for each period are shown for humpback (S.c.) and snubfin (O.h.) dolphins. In the table C.A. = core areas (PC = Port Curtis, KB = Keppel Bay and NR = Northern Region), S = species, Mean = overall mean per region, SD = standard deviation, 1 st = mean hourly sighting rate for the first sampling period, 2 nd = mean hourly sighting rate for the second sampling period, 3 rd = mean hourly sighting rate for the third sampling period, Hrs = total hours of survey, Sch = number of schools sighted Table 3.3 Kruskall-Wallis (Χ 2 ) and Mann Whitney (MN) test results with significance value (P) and degrees of freedom (df) for the analysis of differences in humpback dolphin sighting rates among regions (PC = Port Curtis, KB = Keppel Bay, NR = Northern Region) and within regions among sampling periods and seasons. The significant values (P = 0.05) are highlighted in italics. As in the NR surveys occurred only in winter seasonal comparison was not possible Table 3.4 Kruskall-Wallis test (Χ 2 ) results for the analysis of differences in the age composition of humpback dolphin schools among regions and within region among sampling periods. The only statistically significant value (P < 0.05) is highlighted in italics. In the table df = degrees of freedom Table 4.1. Summary of association pattern analysis of humpback dolphins for each selection criteria. In the table NS = number of sightings, n = number of individuals in the analysis, Q = Modularity Index, HWI = Half Weight Index, SD = standard deviation, Real = Mean HWI for observed data, Random = Mean HWI for permuted data, % = percentage of observed associations for real and random data, P = significance value Table 4.2. Summary of association pattern analysis of snubfin dolphins for each selection criteria. In the table NS = number of sightings, n = number of individuals in the analysis, Q = Modularity Index, HWI = Half Weight Index, SD = standard deviation, Real = Mean HWI for XVII

19 observed data, Random = Mean HWI for permuted data, % = percentage of observed associations from real and random data, P = significance value Table 4.3. Summary of association pattern analysis of humpback dolphins among communities. In the table NS = number of sightings, n = number of individuals in the analysis, Q = Modularity Index, HWI = Half Weight Index, SD = standard deviation, Real = Mean HWI for observed data, Random = Mean HWI for permuted data, % = percentage of observed associations for real and random data, P = significance value Table 4.4 Habitat use availability analysis for humpback and snubfin dolphins. Category = depth range in m at lowest astronomical tide, Av = proportion of microhabitat availability; n= number of detected dolphins; a r = Manly s alpha index; Dr= deviation between a r and 1/n R. There were no areas deeper than 20 m in the Port Curtis area Table 4.5 Overall mean, mean of the 95% CI and ranges for distance to land, to intertidal areas and deeper waters of humpback and snubfin dolphins sightings in Keppel Bay and Port Curtis study area and for random locations. Values are in kilometres unless specified for meters (m) Table 4.6. Effect sizes (μ-μ r ), 95% confidence intervals, followed by corresponding P-values from randomisation tests for distance to land, distance to intertidal area, and distance to deeper waters. In the table species: S,c, = Sousa chinensis and O.h. = Orcaella heinsohni; Area: PC = Port Curtis and KB = Keppel Bay Table 5.1 Polymorphic sites within mtdna control region sequences for each haplotype (H). Numbers indicate the position along the 461 base pairs long fragment. N = number of samples with the specific haplotype. Loc. = population where each different haplotype is found: PC (Port Curtis), KB (Keppel Bay), NGSS (Northern Great Sandy Strait), SGSS (Southern Great Sandy Strait) Table 5.2 Genetic variation at each microsatellite locus for each population. The numbers of individuals analysed for each population are indicated below the population names. The allelic richness (AR.), heterozygosity observed (Ho), and heterozygosity expected (He) are reported Table 5.3. Test results for Hardy-Weinberg equilibrium including P-values and F IS values for all loci and population comparisons. Significance level (P) after Bonferroni correction was Significant P values are shown in bold Table 5.4. Variance components (var) and permutation probabilities for AMOVAs. Dataset was partitioned according to regions (Capricorn Coast and Great Sandy Strait). df = degrees of freedom, SSD = sum of squares, permutation probability is given for the probability that randomised value > observed value. Results for both evolution models are presented Table 5.5. Variance components and permutation probabilities for AMOVAs when dataset was partitioned according to populations (KB, GLD, NGSS, SGSS). df = degrees of freedom, XVIII

20 SSD = sum of squares, permutation probability is given for the probability that randomised value > observed value. Results are present for both evolution models Table 5.6 On the lower triangular matrix genetic differentiation among pairwise humpback dolphin populations using F ST values are reported, while R ST values are reported in the upper diagonal (*P < 0.05, **P < ). Nm values (shown in parentheses) are reported underneath F ST and R ST values Table 5.7 Estimated posterior probabilities for K that varies from 1 to 4, based on results from the Admixture model without/with information on sampling location, and % of membership for each predefined population in the inferred clusters. The consistency of the results was tested through 5 independent runs for each K number of tested populations. Only results that maximised lnpr(x/k) for each K value are presented in the table Table 5.8: Test results for Hardy-Weinberg equilibrium including P and F IS values for all loci and populations. M indicates monomorphic loci for a particular population. Significant p value after Bonferroni correction was Table 5.9 Genetic variation at each microsatellite locus for each population. The numbers of individuals analysed for each population are indicated below the population names. The allelic richness (AR), heterozygosity observed (Ho), and heterozygosity expected (He) are reported Table 5.10 Variance components and permutation probabilities for AMOVAs when the dataset was partitioned according to populations (KB, CB, HB). df = degrees of freedom, SSD = sum of squares, permutation probability is given for the probability that randomised value > observed value. Results are presented for both evolution models Table 5.11 On the lower triangular matrix genetic differentiations among pairwise populations using F ST values are reported, while R ST values are reported in the upper diagonal (*P < 0.05, **P < ). Relative estimates of Nm are reported in parentheses underneath each F ST and R ST values. The negative F ST values among Cleveland Bay and Halifax Bay is an artefact of the method used which arise when there is no differentiation among populations Table 5.12 Estimated posterior probabilities for K that varies from 1 to 3, based on results from the Admixture model without/with information on sampling location, and % of membership for each predefined population in the inferred clusters. The consistency of the results was tested through 5 independent runs for each K number of tested populations. Only Results that maximised lnpr(x/k) for each K are presented in this table Table 6.1. Number of dolphins identified per study site and distinguished by dataset. In the Northern Region no juveniles showed long term marks therefore the total number of marked individuals coincides with the group M XIX

21 Table 6.2. Validation of the assumptions involved in Jolly-Seber capture recapture models used for the estimation of population sizes of humpback dolphins along the Capricorn Coast Table 6.3 Abundance estimates of humpback dolphins in the Capricorn Coast between January 2006 and October In this table only the model that best fitted the data according to the AICc is shown. In the table Mt = model type (2Gr = 2 group model; 1Gr = 1 group model); np = number of estimable parameters in the model; D = dataset (A = adults; J = juveniles). Other notations: n = number of animals captured; Ň= estimates of marked animals; SE = standard error; CV = coefficient of variation; CI = confidence interval; θ = proportion of identifiable animals; N = estimate of total population size after correcting for proportion of identifiable individual Table 6.4. Seasonal abundance estimates of humpback dolphins in the Capricorn Coast between October 2006 and October 2008 using one group model. Letters and symbols are used as described in Table 6.3 and in the test. In the second column S = season either wet or summer (W) and dry or winter (D), the number indicating the capture occasion Table 6.5 Seasonal abundance estimates of humpback dolphins in the Capricorn Coast between October 2006 and September 2008 using the 2 groups model. Letters and symbols are used as described in Tables 6.3, 6.4 and in the test Table 7.1. Population estimates of marked snubfin individuals (Ň) for the 8 closed population models. QAICc = Quasi-Akaike Information Criterion value corrected for small numbers, NP = number of parameters, SE = standard error, 95% CI = 95% confidence interval, CV = coefficient of variation Table 7.2. Total population size (N) of snubfin dolphins for the 8 closed population models. The proportion of marked individuals was θ = In the table SE = standard error, 95% CI = 95% confidence interval, CV = coefficient of variation Table 7.3. Marked (Ň) and total population size (N t ) for each capture occasion of the Keppel Bay snubfin dolphin population, estimated using M tb close populations model. In the table SE = standard error, 95% CI = 95% confidence interval, CV = coefficient of variation, and θ = proportion of marked individuals Table 7.4. Overall marked (Ň) and total population estimates (N) of snubfin dolphins in Keppel Bay using POPAN parameterisation for open populations. The proportion of marked individuals θ was In the table SE = standard error, 95% CI = 95% confidence interval Table 7.5. Marked (Ň) and total population size (N) for each of the four capture occasions t of the Keppel Bay snubfin dolphin population, estimated using POPAN open populations model. In the table SE = standard error, 95% CI = 95% confidence interval, CV = coefficient of variation, and θ(t) = proportion of marked individuals for a particular capture occasion. 150 XX

22 XXI

23 Chapter 1 Introduction In this chapter I provide some background on coastal dolphins and briefly introduce the concept of a Management Unit and its importance for the conservation of regional populations. In this Chapter I also outline the research aims and objectives and the structure of the thesis.

24 1.1 Introduction Estuaries and coastal environments have been focal points of human settlement and marine resources use throughout history. Centuries of overexploitation, habitat transformation, and pollution have obscured the total magnitude of estuarine degradation and biodiversity loss, and have undermined their ecological resilience (Lotze et al. 2006). With increasing recognition of their essential roles for human and marine life, estuaries, coastal zones and species living in those ecosystems have become the focus of research and conservation efforts. River and inshore-estuarine dolphin species are among the world s most threatened mammal species (Perrin 1999, Reeves at al. 2003, Smith et al. 2006, Harrison et al. 2009). Due to their restricted inshore distribution, often near to detrimental human activities, these species are threatened in many ways such as through overfishing, incidental catches, vessel traffic, habitat loss and degradation and water pollution. Unlike many other marine species, inshore-estuarine dolphins are unable to withstand high rates of natural or anthropogenic mortality, and are vulnerable to rapid population decline due to their low reproduction rate, low fecundity, and long parental care (Taylor 2002). The recent extinction of the Baiji dolphin (Lipotes vexillifer) is a sad example of the consequences associated with unregulated human activity in rivers and estuarine areas. In the 1950s the Yangtze River, in China, still supported a population of about 6000 individual Baiji dolphins; however, the population rapidly declined to few hundred in the 1980s, and was declared functionally extinct in 2006 (Turvey et al. 2007). The lack of knowledge on many aspects of the ecology of most dolphin species is one of the major obstacles to their effective conservation and management. In this regard, this research aims to fill some important knowledge gaps and will help define the conservation status of two estuarine-coastal dolphin species potentially endemic to Australian waters, the Australian snubfin dolphin (Orcaella heinsohni) and the Indo-Pacific humpback dolphin (Sousa chinensis) in the coastal waters of the Capricorn region of the Great Barrier Reef, Central Queensland, Australia. This region is referred to as the Capricorn Coast throughout this thesis, and is described in Chapter 3. 2

25 1.2 Rationale for this research The Australian snubfin dolphin is the only cetacean species currently recognised as being regionally endemic to Australia and possibly Papua New Guinea (Beasly et al. 2005), but there is increasing genetic evidence suggesting that the Indo-Pacific humpback dolphin in Australia (hereafter referred to as the humpback dolphin) may also be a distinct species (Frère et al. 2008). Therefore both species have extremely important biodiversity values. Moreover, due to their strictly coastal-estuarine distribution and their functional role as upper trophic level predators, they also have a fundamental ecological role in the function of coastal ecosystems. As top predators they exert strong influence on their prey populations, hence a significant reduction in their population size or extinction can have far-reaching consequences for the structure and functioning of their coastal ecosystems (Borrvall and Ebenman 2006, Creel and Christianson 2008, Heithaus et al. 2008). Information from the only two long-term studies completed in Australia indicate that populations of both species are numerically small and that these dolphins live in geographically and potentially genetic isolated populations or metapopulations (Parra et al. 2005, Cagnazzi et al. 2011). This makes them particularly vulnerable to human-induced disturbances in coastal ecosystems and potentially susceptible to local extirpation if rates of population recovery and dispersal between populations are adversely affected (Hanski 1998). These altered demographics can propel already endangered species into an inbreeding spiral that in turn, reduces individual survival and fecundity, and ultimately, reduces their population sizes. This process can become self-perpetuating and may lead to local extirpation (Holycross and Douglas 2007). Without better knowledge on their abundance, population structure and gene flow and hence an understanding of how to manage the metapopulations, there are serious concerns about the conservation and longterm survival of these species in Australian waters (Parra et al. 2005). As a result of insufficient information being available for both species, present conservation efforts are limited to a list of potential threats and to the identification of humpback and snubfin dolphins as priority species for conservation, as outlined in the Queensland Government s 'Back on Track species prioritization framework'. The 'Back on Track species prioritisation framework' was designed by the Queensland Department of Environment and Resource Management, to prioritize all species and to identify the major threats for each species, regardless of their current classification under the Queensland Nature Conservation Act 1992 (NCA) or the Commonwealth Environment Protection and Biodiversity 3

26 Conservation Act 1999 (EPBC). In the 'Back on Track species prioritization framework' it was recognised that a revision of the conservation status of the humpback and snubfin dolphins, currently classified as rare in Queensland and migratory under the EPBC Act 1999, into a more appropriate threatened status was needed. However, due to the absence of information on humpback and snubfin dolphins from most of their Queensland and Australian range, a review of their conservation status could not be undertaken at that stage. These conservation concerns have increased the number of studies on both species in Australia, nevertheless basic information such as distribution and abundance are still lacking throughout most of their range (Parra and Corkeron 2001, Parra et al. 2002, Parra et al. 2004, Parra 2006, Parra et al. 2006, Cagnazzi et al. 2011). This is the case for the coastal area of Central Queensland, which is the area of focus for this research. This region occurs outside the common tourism route, and has undergone large industrial development, with significant negative impact on the coastal habitats (see Chapter 3 for more details). No previous studies have been undertaken on humpback and snubfin dolphins in this region. 1.3 Introduction to Management Units. Genetic analyses allow differences between populations to be quantified, ranging from reciprocal monophyly through substantial but incomplete phylogenetic separation, to minor but statistically significant differences in allele frequency. Populations that do not show reciprocal monophyly for MtDNA alleles, yet have diverged in allele frequency, are significant for conservation in that they represent populations connected by such low levels of gene flow that they are functionally independent (Moritz 1994). For conservation purposes such populations are classified as Management Units (MUs). The identification of such MUs in dolphin populations is the main focus of this thesis. MUs are the logical unit for population monitoring and demographic study. Therefore, the identification of such management units and a better understanding of the genetics and ecological relationships among populations, are central to the management of natural populations and are crucial for monitoring the effects of human activity upon species abundance (Palsboll et al. 2007). The search for an appropriate method to unambiguously identify management units for conservation purposes has been one of the primary areas of focus of conservation ecologists and biologists (Fraser and Bernatchez 2001). Based on the need to increase the protection of isolated populations that require separate management, and to provide an objective prioritisation approach to identify such populations, Ryder (1986) 4

27 introduced the concept of Evolutionary Significant Units (ESUs), a concept that has gained legal status in the US Endangered Species Act (Ryder 1986, Waples 1991). At present, ESU is defined as group of conspecific individuals that has substantial geographic and reproductive isolation, which has led to adaptive differences so that they represent a significant evolutionary component of the species (Palsboll et al. 2007). The term Management Unit (MU) is a slight modification of the previous concept, and it was developed to include in conservation processes those isolated populations whose population dynamics depends primarily on local birth and death rates rather than immigration (Palsboll et al. 2007). The first criterion to delineate an MU was introduced by Moritz in 1994, who defined MUs as...populations with significant divergence of allele frequencies at nuclear or mitochondrial loci regardless of the phylogenetic distinctiveness of the alleles (Moritz 1994). The distinction between ESUs and MUs is as fine as it is important because it affects the ways in which genetic evidence is obtained and interpreted to define such units. Different approaches have been used to interpret the data to define an ESU or MU, which has led to numerous debates (Fraser and Bernatchez 2001). The conclusion is that there is no a single approach applicable to all situations, but that the best available biological information should be used in making such decisions on a case-by-case basis and that common sense should prevail (Fraser and Bernatchez 2001). Humpback and snubfin dolphins from the Capricorn Coast are at or near the southern limit of their distribution (Parra et al. 2002, Parra et al. 2004, D. Cagnazzi unpublished data), and available information indicates that both species are likely to occur in geographically distinct populations, rather than forming a large population that moves along the coast. If this is true, the conservation of these species at a local level cannot be guaranteed without the recognition of the local populations as distinct management units, and without the development and application of management plans that identify and attempt to manage specific local threats. 1.4 Research aims and objectives The primary aims of this research project are to apply a multidisciplinary approach to identify potential Management Units of humpback and snubfin dolphins in inshore regions of the Capricorn Coast and to assess their conservation status. To achieve these aims, this thesis provides new data on genetics and distribution that are used to identify potential 5

28 management units, which coupled with data on abundance and habitat use will help to better define the conservation status of humpback and snubfin dolphins in the Capricorn Coast region of Queensland. This information will assist management agencies and local conservation groups to develop more effective conservation plans for these species in this region. 1.5 Structure of the Thesis In order to achieve the aims described above, this thesis will address the following objectives: Objective 1. To investigate the occurrence and spatial distribution of Australian snubfin and Indo-Pacific humpback dolphins in the Capricorn Coast (Chapter 3). In Chapter 3, I used information collected during dedicated surveys in the Capricorn Coast, Central Queensland to determine distribution and identify patterns in the occurrence and school dynamics of Australian snubfin and Indo-Pacific humpback dolphins. Sighting rates and school dynamics were compared among survey periods and seasons to investigate if local populations were affected by seasonal changes or if they were year-round residents. This chapter also outlines the survey techniques applied during boat based surveys done between 2006 and 2008 in the Capricorn Coast. Objective 2. To analyze social structure and to define home range and habitat preferences of humpback and snubfin dolphins (Chapter 4). The association and ranging patterns of both species were examined to test the hypothesis that humpback and snubfin dolphins each form a single large population, against the alternative hypotheses that these dolphins consist of various communities with fine-scale structure (Rossbach and Herzing 1999), or represent isolated populations. Results from the association pattern analysis were then used to identify the distribution ranges of communities or populations using Kernel methods. Habitat selection will be then determined for each community using Manly Alpha Index (Manly et al. 2002), Monte Carlo Chain methods and Randomisation procedures. Objective 3. To determine genetic population structure and gene flow among geographically separated communities of humpback and snubfin dolphins in Queensland waters (Chapter 5). 6

29 In this chapter, mitochondrial DNA control region and nuclear microsatellite markers were used to identify potential management units based on the definition provided by Moritz (1994). Population genetic structure and gene flow were examined using samples of humpback and snubfin dolphins collected from geographically distinct locations in Queensland waters. Objective 4. To obtain accurate population estimates of humpback and snubfin dolphins in the inshore coastal waters of the Capricorn Section of the Great Barrier Reef Marine Park (Chapter 6 and 7). In this chapter Mark-Recapture methods were used to obtain accurate population estimates of snubfin and humpback dolphins living in the Capricorn Coast. 7

30 Chapter 2 Summary of current knowledge: Australian snubfin dolphin and Indo- Pacific humpback dolphin In this chapter I summarise the current available knowledge on taxonomy, morphology and life history, distribution, habitat preferences, social structure, conservation status and threats to Australian snubfin and Indo-Pacific humpback dolphins an emphasize on research in Australian waters. 8

31 2.1 Summary of current knowledge: Australian snubfin dolphin and Indo-Pacific humpback dolphin Taxonomy The taxonomic classification of dolphins in the genera Orcaella and Sousa has been highly confused and controversial (Jefferson and Karczmarski 2001, Jefferson and Waerebeek 2004, Beasley et al. 2005). However recent genetic evidence coupled with morphological data have provided a clearer understanding of the inter- and intra-specific relationships at both genus and species level. In 1999 the Irrawaddy dolphin (Orcaella brevirostris) was included in the Delphinidae family (LeDuc et al. 1999). Previously it was considered to be part of the Monodontidae family which includes the Beluga (Delphinapterus leucas) and the Narwhal (Monodon monoceros) (Arnold and Heinsohn 1996). Until 2005, the genus Orcaella was consider monotypic, with the Irrawaddy dolphins (O. brevirostris) being the only species (Rice 1998). In 2005 a study on the external and skull morphology, colour pattern, and mitochondrial DNA control region, including samples throughout the species range, showed that the Australian ecotype is a different species, now named the Australian snubfin dolphin (Orcaella heinsohni) (Beasley et al. 2005). The taxonomic history of the genus Sousa has been more confused. Although the external appearance of Sousa humpback dolphins is similar in some respects to the genus Sotalia, a small delphinid living in South America, molecular studies indicate that Indo-Pacific humpback dolphins (Sousa chinensis) are more closely related to tropical oceanic dolphin species such as those of the genera Stenella, Delphinus, Tursiops, and Lagenodelphis (LeDuc et al. 1999). As result of variation in colour pattern within the genus Sousa, up to 7 nominal species have been proposed by various authors (Rice 1998). At present, the genus is recognised as consisting of a single highly variable species (Sousa chinensis) subdivided into three nominal subspecies: S. chinensis chinensis (Pacific Ocean), S. chinensis plumbea (Indian Ocean) and Sousa teuszii (Atlantic ocean) (Ross et al. 1994, Rice 1998). A more recent study on skull morphology provided stronger evidence in support of the subdivision of the genus Sousa into three species corresponding to the chinensis, plumbea and teuszii ecotypes (Jefferson and Waerebeek 2004). However, patterns of cranial variation were 9

32 conservative and no taxonomic revision was recommended at that time. A current genetics study conducted by Rosenbaum and colleagues (Rosenbaum, personal communication) is corroborating this subdivision, but with the separation of the Australian population as a fourth distinct subspecies or species. This distinction is further supported by a recent mitochondrial DNA study (Frère et al. 2008), which indicates that humpback dolphins in Australian waters may represent a different species from populations elsewhere, and thus a formal taxonomic review is required External morphology and life history of snubfin and humpback dolphins in Australian waters. The snubfin dolphin is a medium size delphinid (Fig. 2.1), and adult females measure up to 230 cm while males reach 270 cm in length, and weigh up to 114 and 133 kg respectively (Arnold and Heinsohn 1996, Beasley et al. 2005). The head is rounded and lacks an obvious rostrum, while there is a distinct neck with creases. The dorsal fin is small, about 20 cm high and is located on the posterior half of the body. Colour is subtly three-tone, with a dark brown cape, creamy abdominal field and an intermediate brownish colour on the side (Beasley et al. 2005). Humpback dolphins are substantially larger, measuring up to 280 cm with apparently no external morphological difference between sexes, and they weigh between kg (Fig. 2.2) (Ross et al. 1994, Jefferson 2000). The dorsal fin is small, triangular in shape and sits on top of an almost invisible hump, which is a characteristic feature of this species in other regions (Jefferson and Karczmarski 2001). In Australia, adult humpback dolphins are a dark gray with a white flecked belly, while the dorsal fin, melon and rostrum whiten with the age (Jefferson and Karczmarski 2001). Figure 2.1 Australian snubfin dolphin, Orcaella heinsohni, from the Fitzroy River, Queensland. 10

33 Figure 2.2 Indo-Pacific humpback dolphin, Sousa chinensis, from the Fitzroy River, Queensland Australia. There are no long term studies of the life history of snubfin and humpback dolphins in Australian waters. Based on age studies on captive animals, both species may live for more than 20 years (Heinsohn 1979, Marsh et al. 1989, Jefferson and Karczmarski 2001). Most of the information available on the life history of humpback dolphins comes from populations in South Africa (Cockcroft 1989) and Hong Kong (Jefferson 2000). In South Africa and Hong Kong, the gestation period of humpback dolphins lasts months, lactation may last more than 2 years, sexual maturity is reached at 10 years of age for females and years for males, and a 3-year calving interval has been suggested (Cockcroft 1989, Jefferson 2000). For snubfin dolphins age at sexual maturity and calving interval are unknown while gestation lasts 14 months in captive animals (Ross 2006) Distribution of Australian snubfin and humpback dolphins Snubfin dolphins occur in tropical to subtropical waters of Australia and possibly Papua New Guinea (Beasley et al. 2005). In Australia, snubfin and humpback dolphins have been recorded from North Western Australia through the Northern Territory and Queensland with humpback dolphins extending a bit further South than snubfin dolphins. More precisely, snubfin dolphins have been recorded from Broome (17 57 S, E) in Western Australia, throughout the entire northern coastline and the Gulf of Carpentaria, and along the eastern coast as far south as the Brisbane River (27 32 S, E) (Paterson et al. 1998, Parra et al. 2002). The southernmost sighting of snubfin dolphins was recorded in June 1997, when two snubfin dolphins were reported swimming in the Brisbane River (Paterson et al. 1998). The carcasses of both specimens were found floating in the upper branch of the river a few days later on the 20 and 27 of July respectively (Paterson et al. 1998). Prior to that, the southernmost record of a snubfin dolphin was from Bundaberg (24 52 S, E), 11

34 and in 2007 a specimen was found entangled and dead in the shark nets near Noosa (Queensland marine wildlife stranding and mortality Database 2007, available at /). Several long term research projects on inshore dolphin species (mainly bottlenose dolphins) have been completed in southern Queensland, but sightings of snubfin dolphins remain very rare. Therefore these southern distribution records are likely to constitute vagrant individuals in search of new areas to colonise. In this thesis using data collected during the field work, a new southern distribution for snubfin dolphins in Australia is proposed (see Chapter 7), with a substantial limitation of their range along the East Coast. Humpback dolphins have a wider range extending from about the Exmouth Gulf in Western Australia, with a few rare records as far south as Shark Bay (Corkeron et al 1997), across the tropical north down to Moreton Bay, Queensland. In addition there have been occasional sightings of humpback dolphins in northern New South Wales (Guido Parra personal communication) Habitat Both humpback and snubfin dolphins are considered strictly inshore coastal and estuarine dolphin species showing a preference for estuaries, coastal inlets and bays. A study conducted by Parra et al. (2006) showed that snubfin and humpback dolphins in Australian waters occur mostly in waters less than 15 m deep, within 10 km from the coast and 20 km from the nearest river mouth. Despite the high spatial overlap and concordance in space use between these species, there are small differences in their microhabitat selection. Snubfin dolphins seem to prefer shallower waters (1-2m) preferably over seagrass beds, while humpback dolphin were more often sighted in water ranging from 2 to 5 m and showed a preference for dredged channels (Parra et al. 2006). Along the Queensland coast humpback and snubfin dolphins have been reported up to 55.6 km and 23 km from the coast and in water up to 30m deep (Corkeron et al. 1997, Parra et al. 2004). However those offshore sighting records are questionable and are not supported by any other reports, and were recorded during aerial surveys, which is an ineffective method for recording these relatively small, shy, and cryptic species Abundance Recent estimates of population sizes of humpback dolphins in Australian waters are available only from two regions in Queensland, the Great Sandy Strait (Cagnazzi et al. 2011) 12

35 and Townsville (Parra et al. 2005). Abundance estimates per population from both regions are well below 100 individuals. A bigger population of about individuals was recorded living in Morton Bay, Southeast Queensland ( N=163 95%CI = ; and N=119 95%CI = ) (Corkeron et al. 1997), but these population estimates are now old and may not reflect the current abundance. Estimates of population size for snubfin dolphins in Australia are only available for the Townsville population (N=64-76 Parra et al. 2005). Estimates are similar to those recorded for humpback dolphins in the same region (Parra et al. 2005). Data indicate that snubfin dolphins in Queensland may tend to live in small geographically isolated populations. This conclusion is supported by qualitative surveys completed between 2006 and 2008 by Guido Parra in Halifax Bay and Princess Charlotte Bay, northern Queensland, and by the author from Cape Palmerston ( S, E) to Airlie Beach ( S, E) Central Queensland, as part of a project aiming to develop spatial models at the State level for both species. Based on the small number of sightings together with the small average school size, population estimates at a regional level (e.g., Queensland) are likely to be in the order of thousands rather than tens of thousands (Parra et al. 2005). Population sizes may be larger in northern Australia, as Freeland and Bayliss (1989) estimated a population of 1000 snubfin dolphins in one area of the Gulf of Carpentaria based on aerial survey data. This estimate has been questioned as a result of the difficulties in identifying dolphin species from the air in turbid waters, and it is likely to be an over-estimate of the real population size (Stacey and Arnold 1999, Parra et al. 2002) Social organisation, site fidelity and movement Throughout their range, snubfin and humpback dolphins tend to occur in schools of fewer than 10 animals, with an average of about 5 and 3 dolphins per school respectively (Corkeron 1990, Parra et al. 2002, Parra et al. 2004, Cagnazzi et al. 2011). Bigger schools composed of about 50 individuals have only been sighted feeding behind trawlers or associated with social behaviour and high prey concentrations (Corkeron 1990, D. Cagnazzi unpublished data). The main intraspecific difference is in the school formation, with snubfin dolphins normally swimming in a very tight formation with extensive physical contact, while humpback dolphins swim in a more open and sparse formation (Parra et al. 2002, Parra et al. 2004). 13

36 There are no studies detailing the association patterns of snubfin and humpback dolphins, thus their social structure remains unknown. However the few data available from Australian waters indicate that humpback dolphins live in a fluid social system, with individuals associating for just short periods of time, with the exception of mothers and calves (G. Parra and D. Cagnazzi unpublished data). This social structure is similar to that recorded in South Africa and Hong Kong where longer term studies have been completed (Karczmarski et al. 1999a, Jefferson 2000). In contrast, snubfin dolphins appear to have a social structure more similar to that of transient killer whales (Baird and Whitehead 2000), with individuals forming more long lasting alliances (Parra and Cagnazzi unpublished data). Little information is available on the movement patterns of both of these dolphin species in Australia and results cannot be extrapolated from populations elsewhere. Resighting patterns of humpback and snubfin dolphins in Townsville suggest a medium to high level of residency and site fidelity, which varies among individuals, while in the Great Sandy Strait humpback dolphins showed stronger site fidelity (Cagnazzi et al. 2011). 2.2 Conservation status and threats Conservation status in Australia Recent studies have indicated that both the Australian snubfin and humpback dolphins are genetically and morphologically distinct and geographically isolated from populations elsewhere. Hence at least one and possibly both species are potentially endemic to Australia and possibly Papua New Guinea. Despite their potential endemic status and ecological importance, both species have been little studied in Australia. Most studies about behaviour and ecology of humpback dolphins have been carried out in South Africa (Saayman and Tayler 1979, Karczmarski et al. 1997, Karczmarski et al. 1999a, Karczmarski et al. 1999b, Karczmarski et al. 2000, Atkins et al. 2004) and in China (Jefferson and Leatherwood 1997, Parsons 1998, Jefferson 2000, Jefferson and Hung 2004). Studies on Irrawaddy dolphins (Orcaella brevirostris) have focused mainly on the ecology of Asian river populations (Kreb 1999, 2002, Smith and Hobbs 2002), whereas the coastal and estuarine populations remain largely unknown (Freeland and Bayliss 1989, Dolar et al. 2002). 14

37 In Australia all cetaceans are protected under State and Commonwealth legislation. The humpback dolphin and the Australian snubfin dolphin are both classified as Rare under the Queensland Nature Conservation Act 1992, listed as migratory species under the Environment Protection and Biodiversity Conservation Act 1999, and as insufficiently known in the Action plan for Australian Cetaceans (Bannister et al. 1996). However, these classifications are based on the very limited information available for both species in Australian waters, and therefore may not appropriately represent the conservation status of humpback and snubfin dolphins in Australia. Furthermore, considering the recent taxonomic evidence indicating that at least one of these species is endemic to Australia and Papua New Guinea (see Section 2.1), information from overseas on similar species cannot be extended to Australian waters. Thus, at present the population status of snubfin and humpback dolphins in Australian waters cannot be reassessed due to the lack of biological and ecological data. The limited information available suggests that populations of these dolphin species are small, localised, and probably declining (Corkeron et al. 1997, Parra et al. 2004). For example, the small population size estimated for Cleveland Bay, north Queensland (Parra et al. 2005) and the Great Sandy Strait (Cagnazzi et al. 2011) indicate that snubfin and humpback dolphins are particularly vulnerable to local extinction. In the Queensland Government Back on Track species prioritisation framework of the Environmental Protection Agency and Threatened Species Committee held in Brisbane in 2008, both humpback and snubfin dolphins were identified as critical priority species, and it was suggested that their conservation status may be more appropriately classified as threatened, or possibly endangered. Due to their coastal and estuarine distribution, snubfin and humpback dolphins are particularly vulnerable to human activities in coastal areas. Habitat degradation, prey loss and overfishing, incidental takes, water pollution, and vessel traffic are known to represent the major conservation and management issues for Australian coastal dolphins (Parra et al. 2002). In particular habitat degradation, prey loss and incidental takes have been highlighted as the three major threats to these species, as assessed by a panel of experts during the Back on Track program. The impact of vessel traffic was not considered as a major threat at a national level because most of the northern Australian coastline has low population density. However, it has been recognised that the impact of this activity in some localised areas in Queensland such as Townsville, Hervey Bay, Gladstone and The Whitsundays has risen substantially in the 15

38 recent past, as a consequence of the increasing number of people using these waterways (Parra et al. 2005, Cagnazzi et al. 2011). Water pollution is also likely to represent a major threat only in some localised areas rather than at a national level, but information on the levels of pollutants and their effects on dolphin s health is scarce, due to the difficulty in accessing fresh carcasses for analyses of pollutants Habitat degradation and loss Habitat fragmentation and loss are critical processes influencing the distribution and abundance of dolphins across their habitat. The two commonly recognised major consequences of habitat fragmentation are increased isolation among already geographically separated populations, and a reduction in the habitat availability (MacArthur and Wilson 1967; Andrén 1994). Habitat area reduction and increased isolation have detrimental effects on species abundance and species movement (Hanski 1998), causing the formation of smaller populations that are more vulnerable to environmental and demographic stochastic events (Fahrig 1997, Foley 1994). Small isolated populations with limited or lack of gene flow from surrounding populations are more prone to genetic drift, inbreeding and bottlenecks that can increase the risk of local extinction. The Great Barrier Reef World Heritage Area (GBRWHA) is significantly affected by coastal development. A main driving factor is the increasing human population in the Great Barrier Reef catchment. Current projections estimate that nearly 1.5 million people will reside in the Great Barrier Reef catchment by 2026, a 40 per cent increase from the current population. Without adequate planning and careful environmental management, this growth will severely affect the habitat quality and extent of habitats suitable for inshore dolphins (GBR outlook report 2009) Overfishing Commercial netting has been identified as a significant risk to inshore dolphin species whose habitat and feeding preferences often coincide with the primary target species for fisheries (Reeves et al. 2004). Worldwide, commercial fisheries are at, or near, full exploitation (Kearney et al. 1996), and overfishing is expected to have significant negative effects on coastal populations of marine mammals in the near future (De Master et al. 2001). In Australia bottom trawling, which is the main fishing method used to catch prawns in Australia, is widely recognized as a major threat to the structure and functioning of coastal 16

39 ecosystems and therefore it is a threat also to humpback and snubfin dolphins whose survival is dependent upon the quality of their habitat. Queensland has almost 20 percent of Australia s commercial fishing fleet (DPIF 2008), corresponding to a commercial fisheries value estimated to be approximately AUS$ 200 million per year (DPIF 2007). However, within the GBRWHA trawling intensity is low with few areas trawled more than a couple of times a year. In 2004, The RAP rezoning has reduced the proportion of dugong habitat in the GBRWHA where trawling is permitted and today only 15% of dugong conservation areas are exposed to trawling (Coles et al. 2008). Unfortunately, in Central Queensland most of the coastal areas are classified as general use zones, and some areas are not within the GBRWHA borders and therefore remain open to trawling and other commercial fishing activities such as the inshore gillnet fishery. Furthermore, the few dugong protection areas occurring in the region don t overlap with critical habitats for inshore dolphins. therefore, most of the catches are still taken close to the coast in waters that often coincide with important feeding habitats for snubfin and humpback dolphins (Pitcher et al. 2002) By-catch Human-related mortality of snubfin and humpback dolphins in Australian waters is thought to be largely attributable to by-catch in ghost nets, and by-catch in inshore gill-nets set across creeks, rivers and shallow estuaries for barramundi (Lates calcarifer Bloch 1970) and threadfin salmon (Polynemus sheridani Macleay 1884 and Eleutheronema tetradactylum Shaw 1804) (Harwood and Hembree 1987, Parra et al. 2002, Parra et al. 2004). In 2004 the GBRWHA was re-zoned to maximize the protection of marine biodiversity through a comprehensive and representative multiple-use zoning regime. During this rezoning, 50% of high priority dugong habitats were closed to commercial fishing activities, including gill and mesh nets used in the Queensland East Coast Inshore Fin Fish Fishery (Grech et al. 2008). Nevertheless in 2007, the cause of death for 11 dolphins along the Queensland coast was directly related to netting, crabbing or fishing lines (Marine wildlife stranding and mortality database 2007, EPA). More information is available about the interaction between coastal dolphins and the shark nets that are set along the coast in an attempt to protect bathers from shark attacks (Paterson 1990). Between 1967 and 1992 at least 544 cetaceans were caught in shark nets (Paterson 1990), with an average of about 20 dolphins caught per year until 1992 (Parra et al. 2002). Between 1992 and 1996 there was a decreasing average number of catches per year ranging between animals (Gribble et al. 1998); however, the level of cetacean interaction with the Shark Control Program equipment has increased in recent years, with more than 20 individuals entangled per year 17

40 between 2005 and 2007, and inshore bottlenose dolphins were the most affected species (Greenland & Limpus 2008). Shark nets have now been removed from most of the Queensland coast and captures of both dolphin species appear to be small if analysed at a state level. However, at a local level the death of even a few individuals could have detrimental consequences on the viability of local populations (Wade 1998). Furthermore, if nets are located along open beach habitats between populations, their presence may affect the movement of dolphins between populations and regions, thereby increasing their geographic, demographic and genetic isolation Pollution As top predators, cetaceans living in coastal waters receiving industrial effluent accumulate high concentrations of anthropogenic contaminants such as pesticides, aromatic hydrocarbons and heavy metals, and this exposure may increase their risk of disease and impair metabolic functions (Evans 2003; Zala and Penn 2004; Guardo et al. 2005). Many pollutants are initially taken up by organisms at the bottom of the food web and are found in increasing concentrations in the tissues and organs of animals at higher trophic levels, with contaminant levels depending on metabolic rates, sex, age and percentage of fat etc.. The toxicological risk of cetaceans is also related to their biochemical vulnerability to lipophilic contaminants (Fossi et al. 1992; Fossi et al. 1997). Tanabe et al. (1982) reported that cetaceans have a low capacity for degradation of organochlorines due to a specific mode of their cytochrome P450 enzyme system. Moreover, since cetaceans do not have sweat and sebaceous glands, fur, or active blood-water exchange via gills, they can be regarded as closed systems in which contaminants can accumulate and act practically without opposition (Marsili et al. 1995). The input of agricultural and urban-sourced pollutants into coastal waters along the Queensland coast has been identified as a major threat to the coastal water quality in the region (Haynes and Michalek-Wagner, 2000). The coastal environment of Central and Northern Queensland receives pollutants into the marine habitat from a variety of sources. These include air and water emissions from several industrial sources, shipping and handling, coal stockpiles, power station corrosion products, urban development, sewage treatment, historical copper mining, oil shale exploration and some natural elements in the landscape (Jones et al. 2005). Consequently, the Great Barrier Reef, especially the inshore area, is being affected by increased sediments, nutrients, pesticides and contaminant levels 18

41 mainly from diffuse agricultural and industrial sources whose origins are outside the Great Barrier Reef therefore more difficult to manage (GBR outlook report 2009) Vessel Traffic There are a number of ways in which boating activities can affect marine mammals. These include direct mortality and injury through boat strikes, disturbance resulting in decreased habitat availability, loss of feeding habitats and indirect interaction with boat debris. Acoustic studies on Indo-Pacific humpback dolphins in Moreton Bay, southeast Queensland, showed that the dolphins acoustic communication and group cohesion are negatively affected by boat traffic and noise (Van Parijs and Corkeron 2001). The Queensland marine stranding and mortality database has 11 records of dolphins being struck by a vessel between 1988 to Although these data may not be considered to be particularly alarming, the number of recreational vessels registered in Queensland has increased from 102,853 in 1990 to more than 150,500 in 2000 with registrations increasing by at least 10% per year (Queensland Environmental Protection Agency 1999, 2000). As more people have boat access to coastal areas and estuaries, the risks associated with vessel traffic will increase. Furthermore, boat traffic also causes more subtle indirect impacts that can be difficult to quantify, but which may have long term consequences such stress leading to changes in the social structure, feeding habits, and reproductive success (Bejder et al. 2006a and Bejder et al. 2006b). 19

42 Chapter 3 Distribution and school dynamics of humpback dolphins and Australian snubfin dolphins in the Capricorn Coast region In this chapter I used information collected during transect surveys along the Capricorn Coast region of the Great Barrier Reef Marine Park, Central Queensland to determine distribution and identify patterns in the occurrence and school dynamics of Australian snubfin and humpback dolphins. Sighting rates and school dynamics were compared among seasons to investigate if local populations were affected by seasonal changes or if they were year round residents. This chapter also describes the survey techniques applied during boat based surveys done between 2006 and 2008 along the coastal waters of the Capricorn Coast region. 20

43 3.1 Introduction Information on animal distribution is integral to wildlife conservation and management. However, despite their inshore distribution and close proximity to industrial and tourist activities, data on the distribution of snubfin and humpback dolphins in Australian waters remain scarce. The distribution of cetaceans is seldom random, but is more often directly driven by physical and environmental variables or indirectly by their influence on prey distribution (Jaquet et al. 1996). Due to the absence of any dedicated field study on coastal cetaceans, there was no information in the scientific literature about the distribution pattern of humpback dolphins and Australian snubfin dolphins in the Capricorn Coast (Capricorn Section of the Great Barrier Reef Marine Park). The only reliable a priori information on dolphins in this region was supplied by Susan Crocetti (former Senior Ranger, Queensland Parks and Wildlife Service, Rockhampton), and Laura Bacon (Masters student from Central Queensland University). Susan Crocetti took photos of both dolphin species during surveys in the mouth of the Fitzroy River (S. Crocetti personal communication). Laura Bacon completed a four week land study in February 2005 to determine the occurrence and behaviour of humpback dolphins in the Gladstone harbour (L. Bacon unpublished data). Other anecdotal reports in the Capricorn Coast region from local fisherman or occasional sightings records were often inaccurate and rarely associated with credible identification at species level to be of much value. In Shoalwater Bay Military Training Area, aerial surveys conducted to assess abundance and distribution of dugongs (Dugong dugon) resulted in no sightings of either dolphin species (Preen et al. 1999, Parra et al. 2002). Therefore during the first period of the study, a standard line transect survey design was used to quantify the occurrence of inshore dolphins along the Capricorn Coast. These data were then used to identify those areas where survey efforts should be concentrated, and those where only occasional surveys were needed. The aims of this chapter are to identify patterns in the distribution and repeated occurrence, and school dynamics (i.e. school size, age category and composition), of humpback and snubfin dolphins and their relationship with seasonal changes in the environment. Comparisons are made to determine intraspecific differences or similarities in the patterns of 21

44 occurrence and school dynamics. This chapter also outlines the main survey methodology used throughout this research project for the boat-based surveys. 3.2 Materials and methods Study area The study area extends for approximately 400 km along the Central Queensland coast and coincides with the Capricorn Section of the Great Barrier Reef Coastal Marine Park (hereafter referred to as the Capricorn Coast). This large area is formed by a variety of coastal habitats, including from south to north: a large inlet (Port Curtis), an extended tidal dominated estuary (Fitzroy River Basin), a large shallow bay (Keppel Bay), and a long open coastline (hereafter referred to as Nine Mile Beach), followed by a series of small bays (Freshwater Bay, Pearl Bay and Pinetree Bay) and two inlets (Port Clinton and Island Head). At the northern end is Shoalwater Bay, a large bay, connected to the coast through a large channel, the Strong Tide Passage (Fig. 3.1). Port Curtis is geographically located at the northern end of the subtropical coastal region and has been largely modified to support major industrial activities. The southern half of Port Curtis is open to the influence of the Coral Sea, while the northern section is a naturally sheltered 30 km long harbour, protected by Facing Island and Curtis Island on the east. Intertidal and shallow subtidal habitats with large mangrove communities (80km 2 ), tidal flats (100km 2 ), and seagrass beds (900ha), cover most of this region (Jones et al. 2005). The tidal range is 4.9 m and tides propagate into the estuary from all four cardinal directions, through various channels that connect the inlet to the open ocean (Witt and Morgan 1999). Fresh water flows may originate from the Calliope and Boyne Rivers and Auckland Creek mainly during heavy rains, and occasionally from The Narrows, a long shallow channel running between the mainland and Curtis Island until the Fitzroy River (Fig. 3.1). The Fitzroy Basin is located partly within the Tropic of Capricorn on the east coast of Australia, between latitudes 21 S and 23 S and longitudes 147 E and 151 E. It is the second largest coastal river system in Australia (Fig. 3.1) (Fentie et al. 2005). With an area of approximately 140,000 km 2 the Fitzroy River is the largest catchment draining into the Great Barrier Reef (Tucker et al. 2001). The river is tidal dominated, with a tidal range of up to 7.5 m and 6 knots in speed. During floods a large amount of sediment, heavy metals, 22

45 nutrients and pesticides are discharged in the Great Barrier Reef via Keppel Bay (Brodie et al. 2003, Furnas 2003). Keppel Bay (Fig. 3.1) is a large shallow bay, up to 12 m in depth and is characterised by clearer waters, coral reefs and various islands. The Keppel Islands are a group of 16 islands located 18 km off the coastal town of Yeppoon and 50 km north-east from the Fitzroy River. Here coral communities are abundant (Van Woisik and Done 1997), but in 2002 a large part of these communities were killed during an extensive coral bleaching event (Diaz-Pulido et al. 2004). The northern section of Keppel Bay is delimited by Coorio Bay. The stretch of coastline from Coorio Bay to Port Clinton, 55 km north, is a series of long sandy beaches of which Nine Mile Beach is the most famous (Fig. 3.1). Port Clinton demarcates the beginning of the Shoalwater Bay Military Training Area. The coastline from Port Clinton until Pinetree Bay (88 km) is unpopulated and is characterised by several small bays interrupted by two inlets, Pearl Bay and Island Head. This is mainly a coastal habitat with clear blue waters, numerous rock reefs and a few coral reefs, and seagrass beds are also present. Shoalwater Bay (Fig. 3.1) is a large V-shaped embayment of about 1000 km 2, of which 57% consists of water less than 10 m deep. The only exceptions are two channels running in a north-south direction and reaching a water depth of 15 m. Numerous groundwater streams, creeks and rivers drain into the area from the east side of the bay. In contrast the western side has a very limited catchment. Shoalwater Bay supports the second largest population of dugongs living in Queensland, and it is classified as Dugong Protection Area level A (Marsh et al. 1996). 23

46 Nine Mile Beach Figure 3.1 Map of the Capricorn Coast study area. The red line represents the 30 m contour depth that was generally used as the offshore limit. 24

47 3.2.2 Survey design Delineation of core areas and transition areas Due to funding limitations and time constraints for the PhD, it was possible to plan only three years of study, resulted in three sampling periods: 1) January-September 2006, 2) October 2006-September 2007, and 3) October 2007-September Due to the limited information on dolphin distribution and limited knowledge of the area, during the first survey period, effort was focused on obtaining the most unbiased information on dolphin distribution along the Capricorn Coast. In the second and third sampling periods, the survey design was modified, using information collected during the first survey period, to increase the number of sightings, and to minimise the time and the survey costs. The study area was initially divided into four core study areas where most of the surveys were done, and transition areas where only occasional surveys were completed. Core areas and transition areas were defined based on inshore dolphin habitat requirements. Inlets, estuaries, or shallow bays were defined as potential core areas. Long open coastlines with extended sandy beaches or steep rocky shores, with no offshore protection and deep waters were defined as potential transition areas. Based on available knowledge and environmental characteristics four regions met the core study area criteria: 1) Port Curtis (PC), 2) Keppel Bay (KB), 3) Northern Region (NR) and 4) Shoalwater Bay (SHB). The East coast of Curtis Island, The Narrows and Nine Mile Beach were defined as transition areas (Fig. 3.2, 3.3, 3.4, 3.5, and 3.6). Survey procedures for Keppel Bay and Port Curtis study sites The Port Curtis study area was located between Wild Cattle Islands (23.96 S, E) in the south, until Worthington Island (23.70 S, E), which marks the beginning of The Narrows (Fig. 3.2). The Keppel Bay study area extended from about Mosquito Creek (23.55 S, E), to Coorio Bay (22.94 S, E), and included the Keppel Islands Group (Fig 3.3). Due to the characteristics of the study area, surveys in Keppel Bay and Port Curtis were conducted using the vessel Sousa, a 5.5 m centre console vessel adapted to surveys of shallow estuarine habitats. While it was possible to survey Port Curtis (Fig. 3.2) in only one day, Keppel Bay had to be subdivided into two four sub-areas: 1) Coorio Bay (CB), 2) Keppel Islands (KI), 3) Fitzroy estuary (FE) and 4) Fitzroy River (FR) (Fig. 3.3). These sub-areas were chosen on the basis of (1) the presence of natural barriers, (2) physical characteristics of the study area, and (3) 25

48 water access. The Coorio Bay survey area extended from Emu Point (23.27 S, E) in the south to Coorio Bay in the north, and it was stratified into one inshore transect (<3 km from the coast), and one offshore transect (3-6 km off the coast). The Keppel Islands subarea included the waters around and between Great and North Keppel islands. The FE transect ran from Keppel Sand (23.30 S, E) in the North, until Sea Hill in the south (23.48 S, E). The FR transect extended from the town of Rockhampton (23.37 S, E) before the river barrage, until Sea Hill. Due to the complexity of this survey area, transects needed to be adapted to local conditions. Therefore, surveys were organized following a general zig zag route, slightly modified daily depending on the weather conditions and tide level. In order to maintain a constant level of probability of sighting dolphins, all surveys were done in calm sea condition that is, <0.8 m swell and Beaufort state <2 at a speed varying between 5 10 knots. From October 2006 the survey design was modified to increase the number of sightings and to minimise the survey costs. The previous transects were modified using the results from the first period of study. In Port Curtis the transect was left unvaried, whereas in Keppel Bay, the previously described 4 transects (CB, KI, FE and FR) were reduced to three: 1) CB, 2) FE and 3) FR (Fig. 3.4). This was done by linking the inshore CB transect to the KI transect. The new southern limit of the CB transect was Rosslyn Bay Marina, and the offshore transect surveyed during the first period was not resurveyed due to lack of sightings. The FE transect was extended until Rosslyn Bay Marina, while the FR transect was not modified. 26

49 Figure 3.2 Port Curtis study area with the general route ( ) followed during boat based surveys done between 2006 and Red circles indicate sightings of humpback dolphins between Jan.-Sept. 2006, while green circles are humpback dolphin sightings recorded between October-September The green flag represent the boat ramp used during the study. 27

50 Figure 3.3 Map of the Keppel Bay study area showing the four transects followed during surveys done between January and September Humpback dolphin sightings observed during the same period are represented with red triangles, while snubfin dolphins are represented with blue circles. The Coorio Bay transect is shown with a black line with two consecutive perpendicular segments ( ), the Fitzroy estuary transect is shown by a black line with one perpendicular segment ( ), the Keppel Island transect by a continuous black line ( ), and the Fitzroy River transect by a series of lines and dots ( ). Green flags represent boat ramps used during the study. 28

51 Figure 3.4 Map of the Keppel Bay study area including transects surveyed during the second and third sampling periods, between October 2006 and September 2008, showing sightings of snubfin dolphins (yellow circles) and humpback dolphins (red triangles). Green flags indicate departure points. The Coorio Bay transect is shown by a series of lines and dots ( ), the Fitzroy estuary transect is shown by a black line with one perpendicular segment ( ), and the Fitzroy River transect was left unvaried from the first sampling period. Green flags represent boat ramps used during the study. 29

52 Survey procedures applied in the Northern Regions and Shoalwater Bay core areas The Northern Region extended from Port Clinton (22.53 S, E) until the beginning of Strong Tide Passage (22.29 S, E) (Fig. 3.5a). The Shoalwater Bay study area extended from Head Creek in the south (22.63 S, E) to Townsend Island in the north (22.21 S, E) (Fig. 3.5b). Because of the complete absence of information on dolphins from the Northern Region and Shoalwater Bay, and limited accessibility to this area, from January to September 2006 a standard line transect survey was conducted to guarantee a uniform coverage probability of the entire area (Fig. 3.5). For these surveys, a parallel stratified sampling design was chosen rather than the more common zig zag survey design, to guarantee a more uniform coverage of the study area (Strindberg and Buckland 2004). To implement the best survey design, the Northern Region was further subdivided into five sub-areas. In each sub-area transect lines were placed 2 km apart (Fig. 3.5a), but with different angles per sub-area (Table 3.1). In Shoalwater Bay transect lines were placed with at a constant angle but 4 km apart (Table 3.1, Fig. 3.5b). The 30 m contour depth, instead of the 20 m contour, considered in the literature to be the critical depth delimiting humpback and snubfin dolphin distribution(jefferson and Karczmarski 2001, Parra et al. 2002), was chosen as the offshore limit to guarantee the inclusion of the total area potentially used by humpback and snubfin dolphins. The software Distance (Thomas et al. 2003), which implements automated survey design algorithms (Strindberg 2001, Strindberg and Buckland, 2004), was used to design the transects. Initially for each sub-area a grid of points, over which coverage probability is assessed, was generated. Then a new design was created specifying the sampler type (line), design class (parallel), the strip width (800 m). The results from each simulation are presented in Table 3.1. Due to the remoteness of this region, a 12 m, powered cat Canomie Dreaming with an elevated observer platform was used for the surveys. Areas that were not accessible with the catamaran were surveyed with the vessel Sousa. All surveys were conducted only in excellent weather conditions, e.g. with Beaufort sea state of 1 or less and no swell. All surveys took place in winter 2006 (May-September 2006) between 06:00 and 14:00 to have similar light wind conditions throughout the survey and to avoid the late morning seabreeze. Transects were surveyed at a speed ranging from 8 to 11 knots, with three observers located on the top deck who were actively searching for dolphins by unaided eyesight or 30

53 using binoculars. Observers were rotated every 30 minutes, resulting in 90 minutes of active observer status and 30 minutes of resting. To collect the data, schools of dolphins were approached by two members of the team using the vessel Sousa while the main vessel was continuing on the transect. Data were collected following standard survey procedures for coastal dolphin species, slightly modified and adapted to the area and species characteristics as explained in paragraph At the end of the first sampling period due to the limited number of sightings, Shoalwater Bay was excluded from the study sites list and it was surveyed only occasionally to collect baseline information for future studies. Further surveys are needed before this preliminary information can be properly assessed. The most likely reason for such low numbers of sightings could be related to the high level of boat traffic and underwater noise present in this region during military exercises. Military activity is conducted for most of the winter season and normally includes thousands of soldiers, watercrafts, ships, fast moving vessels and bombing exercises. In summer when no military activity is planned, weather conditions do not allow safe access to this remote area. Additionally, the few data collected were inaccurate because it was not possible to approach dolphin schools to less than 200 m. As soon as the boat was directed towards the school, dolphins started swimming away at very high speed, making photo-identification and data collection virtually impossible. This behaviour could reflect past and present harassment from military exercises. Therefore the standard surveys were conducted only in three core sites, NR, KB and PC. In the NR, the standard line transect survey design was replaced with a more flexible zig-zag sampling design, as conducted in KB and PC, with a general basic route slightly modified every day to increase sighting probabilities (Fig. 3.6). Surveys were done only using the vessel Sousa, while the vessel Canomie Dreaming was used only as an overnight base unless camping. Survey procedures used in the transition areas Both the Nine Mile Beach and East Port Curtis coast transition areas were characterised by the presence of deep water close to the shore, and the absence of inlets and rivers along the coast. In these transition areas, surveys were conducted between the coastline and the 20-30m contour depth line depending on the area (Fig. 3.7, 3.8), as both of the dolphin species that were the focus of surveys tend to avoid moving further offshore unless the habitat is protected by the presence of islands or shallow waters (Corkeron et al. 1997, Parra et al. 2004, Parra et al. 2006). 31

54 As a result of the limited data collected, transition areas were not surveyed further in the second and third sampling periods. However, some data were collected during occasional crossings of these areas on route to the core study areas. a) b) Figure 3.5 Transect survey lines designed using DISTANCE, that were followed to conduct unbiased preliminary surveys in the Northern Region (a) and Shoalwater Bay (b), between January and September Table 3.1 Summary of the expected and completed survey effort in the Northern Regions and Shoalwater Bay during the first sampling period: column 1) A transect angle, 2) N number of transects per sub-areas, 3) T.L. total transects length 4) T.C. total length including movement between transects, 5) A.T. total area of the substrata, 6) A.C. total area surveyed, 7) P proportion of area surveyed, 8) R number of replicates per sub-areas, 9) Hrs hours of survey, columns 10-12) h, s and b = total number of humpback, snubfin and bottlenose dolphin schools sighted in the Northern Region and Hervey Bay during line transect surveys completed in the first sampling period. Area Transect tracklines (km) Area coverage (km 2 ) Efforts Schools n A N T.L. T.C. A.T. A.C. P R Hrs h s b NR S NR S NR S NR S NR S SHB

55 Figure 3.6 Map of the Northern Region of the study site, showing the survey route followed during the second and third study periods ( ) (October 2006 and to September 2008), the 30 m contour line used as the offshore limit ( ), and humpback dolphin sightings recorded during the first study period (January and September 2006) ( ) and the second and third study periods ( ). 33

56 Figure 3.7 Map of the Nine Mile Beach transition area showing the transect line ( ) sightings of bottlenose dolphins (Tursiops spp.) ( ) and humpback dolphins ( ) observed over the entire study period. The 30 m contour depth line was used as the offshore survey limit. 34

57 Figure 3.8 Map of the East Curtis Island Coast transition area showing the transect line ( ) sightings of offshore and inshore bottlenose dolphins (Tursiops spp.) (green triangle) and the only record of humpback dolphins (red square) and snubfin dolphin (blue circle) over the three years of the study. The 20 m contour depth line was used as the offshore survey limit the research boat was not allowed to go any further offshore under the Marine Safety Queensland boating regulations. The boat ramp used during the study is indicated with a green flag. 35

58 3.2.3 Data collection and definitions Data collected before and after each survey, regardless of the location and the survey design, included the date, starting time and end time, wind speed and direction, sea state, visibility, and total kms surveyed during the day. During each survey, dolphin schools were searched for by two observers with one on each side of the boat, and the boat driver watching ahead. For the purpose of this study, a school was defined as dolphins in relatively close spatial proximity (i.e. each member within 100 m of any other member) that were involved in similar behavioural activities (modified from Parra et al. 2006). After a school of dolphins was sighted and approached, dolphins were photographed until the identification of the right and left sides of each dorsal fin was completed or 15 minutes had elapsed (Würsig and Jefferson 1990). Dorsal fin images were recorded using a Nikon D100 digital camera equipped with an 80-mm to 400-mm zoom lens. Data recorded at each sighting included: species, date, time, geographical location (latitude and longitude) and water depth, recorded with the help of a Hummingbird 595c GPS system connected with a depth sounder. Data on dolphins observed included pod composition (adults, juvenile, and calves) and behaviour (feeding, travelling, socialising and resting). For both species, individuals of between 2-3 m length were classified as adults. In humpback dolphins, the older adults also show whitening of the dorsal fin, rostrum, and body surface while the younger adults are of a uniform gray skin colour (Jefferson and Karczmarski 2001). Juveniles were individuals less than two-thirds of an adult body size, of a uniform light gray skin colour in humpback dolphins, and a light brownish-creamy colour in snubfin dolphins. Calves were individuals with light brown (snubfin) or black to light gray (humpback) skin colour and about half of the adult body length, that were generally associated with an adult, likely to be the mother (Parra et al. 2004). Each school was assigned one of the four behaviour states, feeding, travelling, milling or resting, and socialising or mating, which was determined based on the prevalent activity when the school was first sighted. Behavioural activities were classified using the following criteria: 1) Feeding: dolphins seen actively chasing fishes on the surface, or individuals moving randomly in one specific area showing an unpredictable surfacing pattern often with a 36

59 single breath followed by a medium dive or long steep dives preceded by fluke or peduncle arches. 2) Travelling: a school swimming consistently in one direction for the entire time of the contact showing a constant surface pattern characterised by 2 to 4 breaths followed by short dives. 3) Milling or resting: dolphins apparently not involved in any distinctive behaviour, but swimming in close proximity without following a particular direction or undertaking any social activity. Occasionally individuals were seen resting on the surface. 4) Socialising and mating: individuals involved in an erratic activity without a clear pattern including body to body interaction at the surface, jumps, leaps, tail slap etc.. Schools involved in social activity exhibited long periods at the water surface. To avoid data autocorrelation, only schools composed of different individuals or schools that were sighted more than 1 hour apart and only if they included at least one new individual, were used for the analyses Data analysis Sighting rate, school size and composition As the survey efforts were not uniform across the study period the resighting pattern of humpback and snubfin dolphins was determined by dividing the total number of schools and dolphins of all ages sighted during the day, with the hours of survey effort completed the same day. Variation in sighting rate, school size and composition among regions and within region among sampling periods and seasons was tested using the Kruskall-Wallis test (KW) when more than two groups were compared, and with the Mann Whitney Test when only two groups were compared. Since both tests are non-parametric, assumptions of normality in data distribution and homogeneity of variance don t have to be met. If KW test p values were significant (p < 0.05), a multiple pairwise comparison test, with Bonferroni correction to account for the effect of multiple testing, was applied to determine any significant differences between all the possible pairwise comparisons. To test for variation among sampling periods, data were grouped as follows: January-September 2006, October 2006-September 2007, and October 2007-September To test for differences among seasons, data were pooled into two groups, based on the date of the sighting: 1) summer: October to April and 2) winter: May to September. Based on average air temperature and rain fall, October to April was classified as the summer or wet season, while the period between May and September was classified as the winter or dry season. For 37

60 these analyses, only data from schools collected in core areas between 2006 and 2008, of which the size and composition recorded in the field were positively matched with the school size and composition obtained from the analysis of the photo-id data were used. Also excluded from the analysis were all the dolphin schools observed in association with trawlers. Data collected from Shoalwater Bay and transition areas were also excluded from the analyses, as sightings in those areas were too few to allow any comparison. 3.3 Results Summary of humpback and snubfin dolphin distribution patterns from the first study period: January-September Between January and September 2006 a total of 442 hours were spent searching for dolphins in the four core study areas. Of these, hours were spent between the Northern Region (NR) and Shoalwater Bay (SHB), 90.7 in Port Curtis (PC) and in Keppel Bay (KB) (Fig. 3.9). Additionally 5 surveys searching for dolphins along the Nine Mile Beach and Curtis Island transition areas were completed for a total of 56.6 and 51.4 hours survey time respectively. Humpback dolphin schools (n of schools = 93) were found in all core sites, with the majority of the sightings recorded in Port Curtis (n of schools = 47) (Fig. 3.2). In contrast snubfin dolphin schools (n of schools = 43) were seen only in Keppel Bay (Fig. 3.3). Within the Keppel Bay study area, snubfin dolphins were seen mainly along the Fitzroy River (FR) and Fitzroy Estuary (FE) transects, with 23 and 19 schools sighted respectively, and only one record along the Coorio Bay (CB) transect. In contrast humpback dolphin schools (n of school = 27) were more equally distributed between transects (CB = 7, FE = 11, KI = 0, and FR = 9) (Fig. 3.3). In the Northern Region a total of 15 schools of humpback dolphins were seen, but snubfin dolphins were not observed (Fig. 3.6). Neither species was sighted around the Keppel Islands or in Shoalwater Bay, and only one school of snubfin dolphins and 4 schools of humpback dolphins were seen along transition areas (Fig. 3.7 and 3.8), but always in proximity to the core study areas Overall survey effort Between 2006 and 2008, a total of 20,248 km of transects were surveyed during approximately 1,760 hours of survey time. However, of these only 1,255 hours were in a sea 38

61 Survey efforts (hrs) state < 3 and therefore used for the hypothesis testing. Due to environmental and physical constraints, survey efforts were not uniformly distributed across the entire area and within sampling regions between seasons (Fig. 3.9). A total of hours were spent surveying Keppel Bay, hours surveying Port Curtis, and hours surveying the Northern Region. Survey efforts in Keppel Bay were subdivided per transect as follows: 1) CB-KI = hrs, 2) FE = hrs and 3) FR = hrs. Due to the extreme remoteness and limited access as a result of army exercises during most of the winter seasons, only 97.8 hours were spent completing surveys in Shoalwater Bay. A summary of survey effort and number of schools of dolphins sighted per core area and within areas per sampling period is given in Table 3.2. Within sampling periods, the majority of the surveys were completed during the winter season (73%). In summer, surveys were limited as result of the generally windier conditions, and the late morning seabreeze reaching up to 20 knots (Fig. 3.10). Survey efforts throughout the study period mainly started with Beaufort Sea states 0 (65%) and 1 (27%). However, sea state often varied through the day due to the combined effects of the tide, current and wind SHB NR KB PC 0.00 Summer Winter Summer Winter Summer Winter Jan-Sept 2006 Oct 06-Sept 07 Oct 07-Sept 08 Figure 3.9 Number of surveys completed in Shoalwater Bay (SHB), Northern Region (NR), Keppel Bay (KB) and Port Curtis (PC), pooled for summer months (October-April) and winter months (May-September. 39

62 Survey efforts in hours Wind speed (km/h) Survey efforts in hours Wind speed (km/h) Survey efforts in hours Wind speed (km/h) a) Wind 1 Wind b) Jan Feb Mar Apr May June Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May June Jul Aug Sep Oct Nov Dec 0.00 c) Jan Feb Mar Apr May June Jul Aug Sep Oct Nov Dec 0 Figure 3.10 Survey efforts in hours (bars) completed per month and years in Keppel Bay (a), Port Curtis (b) and Northern Region (c) plotted together with the mean wind speed estimated at 9.00 am (Wind 1: black circles) and 3.00 pm (Wind 2: white squares). A Beaufort sea state of 1 requires a wind speed to be below 15 km/hr. 40

63 3.3.3 Sighting rates of humpback and snubfin dolphins Humpback dolphins sighting rates The overall sighting rate for humpback dolphins was very low, with a mean sighting rate of less than one school sighted per hour of survey (Fig. 3.11a). Mean sighting rates per core study area and within core areas per sampling periods are summarised in Table 3.2. Even if in Port Curtis the mean sighting rate was higher than in Keppel Bay and Northern Region, no significant variation was recorded in the mean sighting rates of humpback dolphin schools among core areas (KW Test: χ 2 = 0.73 df =2, P=0.96) (Table 3.3). Within study areas, the mean sighting rate of humpback dolphin schools was higher in the third sampling period (Table 3.2); however intraspecific differences in the mean sighting rates within areas among sampling periods was significantly different only for Keppel Bay (Table 3.3). A subsequent multiple pairwise comparisons test with Bonferroni correction showed a significant difference in mean sighting rates between the first and second and first and third sampling periods (p < 0.003). Mean sighting rates of humpback dolphins in Port Curtis were not apparently influenced by the season (Summer: SR = 0.57, SD =0.36; Winter: SR = 0.78, SD = 0.38, MW Test = 214 P = 0.14) (Table 3.2). In contrast, in Keppel Bay, humpback dolphin schools were seen significantly more often in winter (Mean SR = 0.36 SD = 0.31) than in summer (Mean SR = 0.15 SD = 0.18) (MW Test = 2091 P=0.0001). Not enough data were available to meaningfully compare humpback dolphin sighting rates among seasons in the Northern Region. Snubfin dolphins sighting rates The overall sighting rate for snubfin dolphins was also very low, with a mean sighting rate of less than one school sighted per hour of survey (Table 3.2, Fig. 3.11a). As snubfin dolphins were seen only in Keppel Bay, comparison among core areas was not possible. Sighting rates of snubfin dolphins in Keppel Bay did not vary significantly among sampling periods (KW Test: χ 2 = 1.54 df = 2, P = 0.56), nor among seasons (Summer: SR = 0.20, SD = 0.25; Winter: SR = 0.24, SD = 0.30) (KB: MW Tests = 1236, P = 0.77). 41

64 Table 3.2 Mean hourly sighting rate per core areas and within areas for each period are shown for humpback (S.c.) and snubfin (O.h.) dolphins. In the table C.A. = core areas (PC = Port Curtis, KB = Keppel Bay and NR = Northern Region), S = species, Mean = overall mean per region, SD = standard deviation, 1 st = mean hourly sighting rate for the first sampling period, 2 nd = mean hourly sighting rate for the second sampling period, 3 rd = mean hourly sighting rate for the third sampling period, Hrs = total hours of survey, Sch = number of schools sighted. C.A. S. Mean (SD) PC S.c (0.38) KB S.c (0.28) NR S.c (0.25) KB O.h (0.28) Hrs Sch 1 st (SD) (0.43) (0.12) (0.25) (0.21) hrs Sch 2 nd (SD) (0.28) (0.19) (0.10) (0.37) Hrs Sch 3 rd (SD) (0.36) (0.34) (0.36) (0.26) Hrs Sch Table 3.3 Kruskall-Wallis (Χ 2 ) and Mann Whitney (MN) test results with significance value (P) and degrees of freedom (df) for the analysis of differences in humpback dolphin sighting rates among regions (PC = Port Curtis, KB = Keppel Bay, NR = Northern Region) and within regions among sampling periods and seasons. The significant values (P = 0.05) are highlighted in italics. As in the NR surveys occurred only in winter seasonal comparison was not possible. Among regions Among sampling periods PC KB NR Χ 2 df P Χ 2 df P Χ 2 df P Χ 2 df P Among seasons Among seasons and regions PC KB NR Χ 2 df P MW P MW P MW P na na na na na 42

65 a) 2.5 b) 18 GL06 GL07 GL08 KB06 KB07 KB08 PC06 PC07 PC08 OH06 OH07 OH08 Dolphin school sightings per hour of survey Number of individuals school GL06 GL07 GL08 KB06 KB07 KB08 PC06 PC07 PC08 OH06 OH07 OH08 c) 14 d) 10 Number of adults per school Number of juveniles per school GL06 GL07 GL08 KB06 KB07 KB08 PC06 PC07 PC08 OH06 OH07 OH08 GL06 GL07 GL08 KB06 KB07 KB08 PC06 PC07 PC08 OH06 OH07 OH08 e) 5 4 Number of calves per school GL06 GL07 GL08 KB06 KB07 KB08 PC06 PC07 PC08 OH06 OH07 OH08 0 Figure 3.11 Boxplots indicating: (a) yearly variation in snubfin (OH) and humpback dolphins (identified with the region code GL, KB, PC) sighting rates, (b) school size and composition, (c) number of adults, (d) juvenile, and (e) calves observed per school. Data were pooled per survey period here with the year in which the major numbers of survey were completed, 06 = first survey period, 07 = second survey period, 08 = third survey period. 43

66 3.3.4 School size and composition School size Accurate estimates were obtained for the size and age composition of 174 out of 179 schools of snubfin dolphins, and 365 out of 437 schools of humpback dolphins. Most of the correct group classifications were made during winter months, with only 9.5% of the humpback dolphin and 23% of snubfin dolphin schools correctly classified during summer months. Schools of snubfin dolphins varied in size from 1 to 13 animals with an overall mean of 2.9 dolphins per school (SD = 2.16) (Fig. 3.11b). The modal school size for snubfin dolphins was 1 (31%) followed by schools of 3 dolphins (27%) (Fig. 3.12). A significant variation was recorded in group size among sampling periods (Kruskal-Wallis Test: df = 2, P < 0.001). In the third sampling period (October 2007 to September 2008) snubfin dolphins were sighted in significantly larger schools (Mean = 3.5 SD = 2.44) than in the previous years (P values for significant pairwise comparisons with Bonferroni correction applied: P(1 st -2 nd ) = 0.02; P (2 nd -3 rd ) < 0.01), while there was no significant difference in the mean school size between the first and second sampling periods (Fig. 3.11b). The school size of humpback dolphins ranged from 1 to 12 animals, with a mean of 3.8 individuals per school (SD = 2.78), with schools of 2 individuals the most commonly sighted school size (24%) (Fig. 3.12). However, larger schools of up to 31 individuals were seen when dolphins were feeding behind trawlers (data excluded from the analysis). There was no significant variation in the sizes of humpback dolphin schools among core areas (HD: KW Test: χ 2 =1.43, df = 2, P = 0.48), nor within core areas among sampling periods (KW Test, KB: χ 2 =2.14, df = 2, P = 0.34; PC: χ 2 =3.86, df = 2, P = 0.14; NR: χ 2 = 1.70, df = 2, P = 0.42) (Fig. 3.11b). As result of the lower percentage of correct group classification available for the summer season, intra-season differences were not tested. School age composition Schools containing adults, calves and juveniles were seen throughout most of the study period. For humpback dolphins there was no significant difference in the school composition among regions (Table 3.4). No significant difference was found in the school composition also within most regions between sampling periods (Table 3.4). The exception was for the number of calves within the Port Curtis core area. In Port Curtis the average number of calves per school in the third sampling period (Mean = 0.7 SD = 0.8) was significantly larger than during the two previous sampling periods (Mean = 0.2 SD = 0.5) (Fig. 3.11e). Overall humpback dolphin schools were mainly composed of adults (Mean = 2.30 SD= 1.74), 44

67 followed by juveniles (Mean = 1.09 SD = 1.15), and calves (Mean=0.4 SD = 0.68) (Fig c,d,e). The number of adult humpback dolphins per school ranged between zero to 12 animals, however when feeding behind trawlers up to 24 adult humpback dolphins were seen in a single large school. Table 3.4 Kruskall-Wallis test (Χ 2 ) results for the analysis of differences in the age composition of humpback dolphin schools among regions and within region among sampling periods. The only statistically significant value (P < 0.05) is highlighted in italics. In the table df = degrees of freedom Among regions Among sampling periods PC KB NR Test Χ 2 df P Χ 2 df P Χ 2 df P Χ 2 df P Adults Juveniles calves During the study, schools of snubfin dolphins containing up to 10 adults were seen, but schools with one adult were the most commonly seen (Fig. 3.12). The average number of adults and calves per school was significantly different among sampling periods (KW Test: adult χ 2 = 25.33, df = 2, P < 0.001; calves χ 2 = 6.46, df = 2, P = 0.04). In the third sampling period, significantly more adults per school were observed than during the first and second sampling periods (P(1 st -3 rd ) = 0.004, P(2 nd -3 rd ) < ), while the difference in the number of calves per school between sampling periods was no more significant after Bonferroni correction was applied. Overall, schools of snubfin dolphins were mainly composed of adults (Mean = 1.9 SD = 1.5), followed by juveniles (Mean = 0.6 SD = 0.8) and calves (Mean = 0.2 SD = 0.5). For both humpback and snubfin dolphins, foraging (48% and 53% respectively) was the most commonly recorded behaviour, followed by travelling (37% and 28%). School size of humpback and snubfin dolphins did no vary significantly with behaviour activity (KW Test: SD: χ 2 =1.06, df = 2, p=0.5; HD: χ 2 =6.44, df = 2, p=0.03 none of the three pairwise comparisons was significant after Bonferroni correction was applied). 45

68 Relative Frequency Relative Freqeuncy KB OH KB SC PC SC NR SC Number of dolphins per school Number of adults per school KB OH KB SC PC SC NR SC Number of juveniles per school Number of calves per school Figure 3.12 Relative frequency distribution of school size and school composition of snubfin dolphins in Keppel Bay (KB OH), and humpback dolphins in Keppel Bay (KB SC), Port Curtis (PC SC) and Northern Regions (PC SC). 46

69 3.4 Discussion Survey limitations and related issues Species detectability in the marine environment is affected by various factors, such as Beaufort sea state, glare and cloud coverage. Limited knowledge of the study area and inexperience of volunteers can also affect sighting probabilities and therefore the quality of the data. From the beginning of the study it was clear that the fieldwork organisation would be a major challenge for this study. The combined effects of strong south-easterly winds and late morning seabreezes affecting this region between October and April limited the survey periods available during the summer, which resulted in substantial differences in the amount of data collected between summer and winter. However, as only data collected in Beaufort sea state <2 were included in the analyses, the bias due to heterogeneities in sighting probability as a result of sea state is expected to be negligible Humpback and snubfin dolphin distribution Various factors has been used to explain dolphin distribution in relation to the habitat structure, but food availability (Lima and Dill 1990) followed by competition and predation risk (Heithaus and Dill 2002) appear to be some of the most important factors affecting dolphins occurrence in coastal marine environments. In north-east Scotland, bottlenose dolphins (Tursiops truncatus) exhibit a heterogeneous use of the coast with most of the records occurring in areas with topographically distinctive characteristics in comparison to surrounding waters, such as areas with deep narrow entrances to coastal inlets that have steep seabed gradients (Wilson et al. 1997). In northeast Queensland, humpback and snubfin dolphins occur in shallow water closer to shore and rivers than would be expected by chance (Parra et al. 2006). Similarly these surveys along the Capricorn Coast suggest that humpback dolphins are geographically clustered with clear preferences for sheltered regions always in proximity to or within inlets, bays and rivers, rather than in habitats consisting of long exposed coastline and open bays. For humpback dolphins, most of the sighting records were obtained in the core areas. However, photo-identification data indicate that some humpback dolphins did move between 47

70 these regions (Chapters 4 and 5). In contrast, snubfin dolphins were sighted only in the Keppel Bay study area but more information is needed to assess the geographic and genetic isolation of this population. The preference of these species for nearshore, estuarine waters has been related to the productivity of these tropical coastal areas (Parra et al. 2006). The limited data available on humpback and snubfin dolphins feeding habits indicate that they are generalist feeders whose diet is determined by their natural constraints such as body size and hunting abilities. In this regard, sheltered coastal inlets and estuaries with their highly diverse and abundant fish communities provide easily accessible food resources (Jefferson and Karczmarski 2001) Sighting rate, school size and composition Identifying which ecological (prey availability, predation, competition) and social factors (mating, parental care, learning) influence the size and composition of animal groups, and how they do so, is a principal theme of behavioural ecology (Janson and Goldsmith 1995). As result, comparative studies of intra and interspecific patterns have become fundamental to the development and subsequent testing of hypotheses explaining group living in animal societies (Ebensperger and Cofre 2001, Chapman et al. 2005). In marine mammals, comparative studies of school size and composition across different species and within species across different regions are rare. The large extent of the study area together with the coexistence of humpback and snubfin dolphins in Keppel Bay, offered an exceptional opportunity to examine intra and interspecific differences in sighting rate, school size and composition between inshore dolphin species. Humpback and snubfin dolphins were sighted throughout the study with no major differences in sighting rates, between species and within species among regions, sampling periods, seasons and sightings. There was some evidence indicating an increasing sighting rate of humpback dolphins in the second and third period of study in Keppel Bay, and this increase was statistically significant if the first and second and first and third sampling periods were compared. The increased sighting rate was probably related to the variation of the survey design after the first sampling period, with less time spent in areas where humpback dolphins were less likely to be found such as the Coorio Bay offshore transect and the Keppel Islands transect, which were cancelled or modified after the first sampling period. In contrast, snubfin dolphins were rarely seen along the CB and KI transects, hence the modification of those transects did not significantly influence their sighting rate. 48

71 Group size and composition did not vary substantially among species and within species among regions, sampling periods, seasons and sightings. The only exception was that snubfin dolphins were seen in significantly larger schools during the third sampling period. This increase in the school size was also observed for humpback dolphins, but it was not statistically significant unless schools sighted in the proximity of trawlers were included. These data were not included in any analysis because there were too few occasions to create a separate group and these data would have biased the overall results. Nevertheless during those two months of trawling activities, dolphins may have been more active, and as they occurred in larger schools they were also more likely to be observed, which could explain the non significant increase in group size (Chilvers and Corkeron 2003). The results indicate considerable stability in the group size and composition with only minimal variation. In contrast, most studies on inshore dolphins show a significant variation in sighting rate, group size and composition (Karczmarski Parra 2006), which has been related to 1) food availability, 2) predation risk (Heithaus and Dill 2002), 3) interspecific competition over food (Heithaus 2001), optimal foraging (Baird and Hill 1996) and 4) social functions (Connor et al. 1002). Food distribution and abundance may constrain the size of groups, with the upper size limited by intragroup competition for food (Wrangham et al. 1993). As food availability increases, the costs of feeding in a large group decreases, and consequently the maximum group size increases. The non significant increase in sighting rate and group size observed in humpback dolphins during the third sampling period, has been related to the presence of fishing trawlers in the area between April and May Due to the heavy rain and floods that occurred in the summer of , prawns, one of the main target species of the inshore Australian East Coast Trawl Fishery, spawned in large numbers in the Keppel Bay area attracting numerous trawlers never seen before in this region. During this period the larger schools of humpback dolphins were seen foraging in close proximity to the trawlers. However, prawns are not a major food item for these dolphins and are usually only found in a small proportion in the stomach contents of humpback and snubfin dolphins compared to teleost fishes and in particular grunt fish (Pomadasys sp.), which is the dolphin s main target species (Parra and Jedensjö 2009). The large number of dolphins observed was the result of the availability of a large amount of prey at limited energy expenditure, rather than a natural concentration of food resources. Trawlers are known to provide a reliable, easily located large source of food for dolphins, through the provision and concentration of prey, while trawler nets are in use and while catches are being sorted. This promotes the formation of 49

72 abnormally large schools of dolphins without costs of intraspecific competition for food (Parsons 1998, Jefferson 2000). These conclusions are supported by the limited variation in humpback dolphins school size and composition observed in the NR and PC study areas where trawling activities were not recorded. The only significant variation was in the mean number of calves per school sighted in Port Curtis during the last sampling period, but this difference was negligible as the mean number of calves per school remained well below 1 and therefore the difference may be the result of just a few births. This behaviour has also been reported in humpback dolphins from Hong Kong (Jefferson 2000), Cleveland Bay and Moreton Bay Australia (Chilvers and Corkeron 2001, Parra et al 2005), but has never been reported for snubfin dolphins. In this study, snubfin dolphins were not seen feeding behind trawlers, nevertheless in the third sampling period group size was significantly larger. As most of the trawling activity occurred at the edge of snubfin dolphin distribution, the absence of competition for food resources with humpback dolphins may have promoted the formation of larger snubfin dolphin schools, with the aim to increase hunting success. Similarly to humpback dolphins, the larger school size may have affected the sighting rate, which was higher in the this period. Predation risk is also considered among the major ecological determinants driving group size, with individuals in larger groups reducing their risk of predation. Studies on tiger shark and bottlenose dolphins in Shark Bay, Western Australia, showed that tiger sharks occurred in shallow waters habitats (< 2.5 m) more often than expected by chance in both winter and summer, and that dolphins formed larger groups in these shallow habitats as a result of this increased predation risk (Heithaus and Dill 2002). Predation risk was believed to be the main reason explaining the difference in group size between snubfin and humpback dolphins in Cleveland Bay, Queensland, Australia (Parra 2006). Due to their preference for shallower habitat (<2 m), snubfin dolphins are more exposed to shark predation than humpback dolphins, which are normally found in deeper water ( >2 m) (Parra 2006). Similar trends were evident from this study in Keppel Bay, where the snubfin dolphins showed preference for shallower waters compared to humpback dolphins (see Chapter 3). However, no significant interspecific difference was found in group size in Keppel Bay, even though large sharks, including tiger sharks (Galeocerdo cuvier), are known to occur in this area, and shark bite marks were evident on both species. 50

73 Social factors also play an important role in determining sighting rate, group size and composition. However, as the number of social interactions observed during the study was very limited, not enough data were available to investigate differences among social and other behaviours. Furthermore, social activities were not able to be distinguished from feeding activities as some dolphins were actively foraging while others were socialising. It is possible that social activity increases when more dolphin schools are attracted to the same area by abundant food resources. The occurrence in a small area of numerous dolphins from various schools, may promote social activities. If so, the effect of social activities on sighting rate, school size and composition could not be distinguished from that the effect of feeding activities. This result is opposite to that found in bottlenose dolphins from Shark Bay, and in humpback and snubfin dolphins from Cleveland Bay (Connor et al. 1992b, Parra personal communication) where social behaviour was a completely distinct activity. Social interactions in the Keppel Bay region may be limited by the geographic isolation and the small population size of both humpback and snubfin dolphins, together with the absence of immigration from surrounding areas. These findings are also consistent with the ecology of both dolphin species. The low sightings rate is a consequence of their low population size, generally shy behaviour, small surface profile and limited surface activity (Parra et al. 2004, Cagnazzi et al. 2011). Indo- Pacific humpback dolphins do not undergo large-scale seasonal migrations, most of the adults exhibit a long-term residence pattern, and hence large variations in the number of adults per school are unlikely. There are not many information on the movement pattern of snubfin dolphin, however the Keppel Bay snubfin dolphin population appears to be formed mainly by resident individuals therefore a significant variation in the sighting rate among sampling periods and seasons is unlikely. Humpback and snubfin dolphins calve throughout the year with no apparent peak in the breeding season, followed by up to 3-years calving interval (Jefferson and Karczmarski 2001); hence variation in the number of calves among regions, sampling periods and seasons is predicted to be low. In humpback dolphins the minimum calf mortality rate to age of 1 year is ca. 20% and the recruitment rate to age of 1 year is 4%, therefore the number of dolphins passing from the juvenile to the adult stage is limited, and any small differences are likely to be undetectable (Jefferson and Karczmarski 2001). This is possibly true also for snubfin dolphin with very few juveniles sighted in the entire study period compared to the number of calves. This information reflects the high level of natal philopatry suggested for both species in Australia (Goodwin 1997, Frère et al. 2008), in which most of the dolphins stay close to their birthplace throughout their lives, thereby approaching a closed population, which may be susceptible to eventual extinction if 51

74 threatening conditions developed, such as sever habitat destruction or limited food resources (Ross 2006). The highly stable school size and composition observed in both species in the study area appears to be a response to a trade-off between food availability and predation risk. In contrast, interspecific competition was rarely observed and social factors don t seem to affect the school size. Future research on these sympatric dolphin species should be directed towards gaining a better understanding about their feeding habits, the distribution and abundance of their prey and predators, and the nature of interspecific interactions with sharks and between both dolphin species. 52

75 Chapter 4 Social structure, home range and habitat preference of Australian snubfin and humpback dolphins in the Capricorn Coast region In this chapter the social structure of Australian snubfin and humpback dolphins was investigated using photo-identification data and association patterns among identified individuals. The level of interaction among individuals was used to distinguish communities within the populations. Results from these analyses were used to determine community home range and habitat preferences for each community. 53

76 4.1 Introduction The social structure of animal populations is defined by the network of interaction between members of a society (Hinde 1976). A property that seems to be common to many networks is the community structure, i.e. the division of network nodes into groups within which the network connections are dense, but between which, are sparser (Newman and Girvan 2004). The definition of community, applied in social animals, is derived, in part, from longterm studies of chimpanzees, Pan troglodytes, in which community members interact within a common home range (Goodall 1986). In this sense, therefore, the term community refers to an assemblage of interacting individuals within a species, rather than the more common usage in ecological literature of an assemblage of interacting species. In dolphin research, a community is defined as a group of dolphins that includes both males and females, that shows long-term site fidelity, relatively high association between members, low association with individuals from other communities, and shares similar habitat and feeding habits (Rossbach and Herzing 1999). This long-term site fidelity is augmented by natal philopatry of both sexes (Connor et al. 2000; Moller and Beheregaray 2004). Despite the lack of prominent geographical barriers in many marine environments coupled with the high dispersal capabilities of dolphins, recent studies have revealed unexpected subdivision of dolphin populations into communities even at very small geographic scales (Wells et al. 1987, Krützen et al. 2004). Dolphin communities are not closed demographic units, as gene flow occurs across community boundaries and individuals may change community membership over time (Wells 1978, Connor et al. 2000). The social structure of an animal population is therefore a fundamental component of its biology, influencing its abundance, distribution, home range, genetic make-up (Pusey & Wolf 1996), and the way that the population exploits its environment (Hoelzel 1993; Connor et al. 1999). Therefore to evaluate fine-scale population structure of dolphins, it is necessary to understand both association and ranging patterns. Ultimately, of course, a full understanding of population structure requires information on rates of gene flow and patterns of genetic structure (Urian et al. 2009). From a conservation perspective it is important to consider the heterogeneity imposed by social structure when assessing the dynamics of populations, as information on community structure may help to describe stocks and boundaries of management units that more 54

77 closely approximate true population units. Distinct social units can react differently to the same environmental and physical factors (Whitehead et al. 2004). For example, different social units, or communities, can develop different foraging strategies and therefore exhibit preferences for different habitats and prey (Whitehead et al. 2004). This can lead to a decrease in the level of interaction among neighbouring communities and consequently to an increasing geographical, demographic and genetic isolation despite the absence of physical barriers (Möller et al. 2007). Information on social structure and ranging patterns is usually obtained by quantifying the level of association among identifiable individuals occurring in close spatial proximity, and investigating how these associations change over time and space. In this study, the photoidentification technique was used to collect data on the identities and distribution of humpback and snubfin dolphins along the Capricorn Coast between 2006 and The association and ranging patterns of marked individuals were examined to test the null hypothesis that humpback dolphins form a single population, against the alternative hypothesis of a population subdivided into separate communities. The same hypothesis was tested for snubfin dolphins. Results from the association pattern analysis were then used to identify community distribution ranges using Kernel methods. Habitat selection was then determined for each community using Manly alpha index, Monte Carlo Chain methods and Randomisation tests. 4.2 Materials and Methods Data collection Refer to details provided in Chapter 3 for information on survey design, data collection and survey effort Identifying a network of communities in the population Hypothesis and definition The initial null hypothesis was that humpback and snubfin dolphins in the Capricorn Coast region of Central Queensland form separate single species populations in which individuals associate randomly with any other conspecific dolphins in the area. The association patterns among individuals and the movement or lack of movement between core areas was used to test the null hypothesis, and to construct the alternative hypothesis based on metapopulation 55

78 theory. In metapopulation theory a population is structured into a network of subpopulations (or communities) connected by migration (Hanski 1998). For the purpose of this analysis, the term community was used as defined by Rossbach and Herzing (1999). The term group was used to define a long-term association of dolphins revealed by the association analysis of dolphin sightings (Chilvers and Corkeron 2002). The term school was defined as one or more dolphins in close-knit formation engaged in the same behaviour (Connor et al. 1998). Two or more dolphins were considered associated if they were sighted within the same school. Association Index and database selection Association patterns among individuals within a school were analysed using the half weight index (HWI), which is the most appropriate method when a pair of dolphins is more likely to be sighted apart than together (Cairns and Schwager 1987): HWI = x/{x + yab + 0.5(ya + yb )}. Where x is the number of schools that includes both dolphin a and b, y a is the number of pods that includes dolphin a, but not dolphin b, and y b is the number of pods that include dolphin b, but not dolphin a, and y ab is the number of encounters including dolphin a and b in different groups at the same time. This association index results in a value ranging from 0 (two dolphins never seen together) to 1 (two dolphins never seen apart). The association index values obtained from each dyad were summarised in a symmetrical matrix describing all the possible pair-wise combinations and their strength. The number of sightings per individual used in similar studies of dolphins varies from a minimum of two to six records (Weinrich 1991, Whitehead et al. 1991, Slooten et al. 1993, Bräger et al. 1994). A preliminary analysis was performed on three data sets corresponding to all dolphins (non calves) sighted on two, three or four occasions. In order to provide independent evidence of association, we considered only resightings that occurred at least one day apart. Each data set was tested for preferred or avoided associations using a permutation test, which randomly generates alternative data sets (Bejder et al. 1998). The test was run the first time with 1,000 permutations and then replicated, increasing the number of permutations by 1,000, until P reached stable values. To describe the population structure, we used the data set that represented the best trade-off between the minimum number of animals, to be representative of the entire population, and the minimum sightings frequencies to maintain the reliability of the data. Analyses were carried out using the Social Analysis module in SOCPROG

79 Distinguishing Communities in a Population From the data set previously selected we constructed two sets of social network. A social network is a structure of nodes connected by any kind of relationship represented by edges. In a dolphin network each vertex represents an individual dolphin and the edge between two vertices indicates the presence of a relationship between two individuals. In the first network, we included all bonds with HWI above zero, so as to include all the recorded associations. In the second network, we applied a cut off technique to remove preferred companionships to eliminate the presence of smaller groups. Preferred associations were identified using the randomisation method of Bejder et al. (1998). We randomly permuted individuals within groups, maintaining both original group sizes and the number of times we observed each animal. After each permutation, we recomputed the HWI of all dyads. After making 4,000 randomisations, we identified as preferred associates those dyads having an observed HWI greater than 95% of the randomized values for the pair. We set the number of permutations to 10,000 because this was the minimum number necessary to achieve a stable P value (Bejder et al. 1998). To assess whether humpback and snubfin dolphin populations are structured in a network of two or more communities we used 1) the Hierarchical Cluster Analysis and 2) the Girvan Newman algorithm (Girvan and Newman 2002, Lusseau and Newman 2004, Newman and Girvan 2004). In the Hierarchical Cluster Analysis, individuals are arranged on one axis and their degree of association on the other. The tree indicates the association index or interaction rate between hierarchically formed clusters of individuals. To subdivide the population into clusters we used modularity 1 method (Newman 2003), which is the difference between the proportion of the total of the association indices, or interaction rates, within clusters and the expected proportion given the summed association of the different individuals (Whitehead 2001). Modularity greater than about 0.3 is usually considered to indicate useful divisions of the data (Newman 2003). The Girvan Newman algorithm breaks the network into a minimum of two communities up to as many communities as the number of dolphins identified. The measure used in this algorithm to quantify the strength of association among individuals is called betweenness (Freeman 1977). Betweenness quantifies the strength of association between pairs of individuals by counting the number of times an edge was crossed when moving on the network. Because dolphins tend to associate more often with members of the same community rather than with dolphins from surrounding communities, the number of edges that connect all individuals within a community should be higher than the number of edges existing between communities. To select the division that provided the most edges within 57

80 communities and the least between, we used the modularity index, Q (Newman 2003). Q is defined as the difference between the proportion of the total association within clusters and the expected proportion, given the summed associations of the different individuals. A Q value greater than about 0.3 is considered to indicate functional division. The best division is indicated by the first Q maxima Home range The Utilization Distribution (UD) is a probability density function that describes the relative use of space by an animal, within a defined area, based on a sample of animal locations (Van Winkle 1975). Individual animals were tracked by means of photo-identification (e.g. Parra and Corkeron 2001), but as the number of relocations for most animals was insufficient to use true probabilistic methods to estimate UDs reliably for each individual, UDs were calculated at the community level. To calculate the 95% UD at community level the same dataset used in the social structure analysis was used. Sightings of schools were pooled together based on the community membership of the dolphins within the school (i.e., schools composed exclusively by individual dolphins from the same community). Schools composed of members of more than one community were used to estimate representative ranges of each community represented in the school. Geographical positions of schools were converted into an ArcMap v 9.1 (Environmental Systems Research Institute), and Hawths Tools were used to estimate a fixed kernel UD for each species (Hooge & Eichenlaub 1997). Kernel ranges of 95% (representative range) probability of occurrence were calculated using smoothing parameters calculated via the least squares cross-validation procedure (Seaman et al. 1999). Home ranges were estimated using the Kernel estimator, because it is one of the best estimators for calculating the UD. We used the 95% UD because it is considered to be the most robust estimator of animals home ranges (Worton 1989). To measure the extent of space sharing between communities, the per cent area overlap (PAO) between the representative ranges (i.e. 95% kernel range) (Atwood & Weeks 2003) of both species was computed as: 58

81 PAO = A i,j A i A i,j A j 0.5 Where A i,j is the area of overlap between two neighbouring dolphin communities, while A i and A j are the area of the communities representative range under analysis (modified after Parra 2006) Habitat use Study area and habitat definition A depth grid with cell resolution of 50x50 m was created for the Keppel Bay and Port Curtis core areas using the inverse distance weighting interpolation in ESRI Arc Info 9. Spatial bathymetry data for these two regions were obtained from the Fitzroy Basin Association, Marine Safety Queensland and from data collected during boat surveys. Unfortunately, not enough depth data were available to create a low resolution depth grid for the Northern Region. Therefore habitat preference was tested only for Keppel Bay and Port Curtis core areas. For Keppel Bay and Port Curtis core areas, a categorical variable depth grid was then created for six depth categories (r): 0-2 m (intertidal area), 2-5 m (shallow interior), 5-10 m (shallow subtidal), m (deeper waters 1), m (deeper water 2) and m (deeper waters 3). Depth was measured at the lowest level of astronomical tide as shown in the navigation chart, and these depths were used to recreate the habitat structure. A depth grid modelled from observed depth would be confounded by varying tidal states along the transects and over time. Therefore the observed depth would not to be a good estimator of dolphin habitat preference. Data originally in WGS 1984 system were projected into Universal Transverse Mercator (UTM) Zone 56S for distance and area calculations. Habitat preference To determine habitat preference of humpback and snubfin dolphins, accounting for the availability of each habitat type, (r) the Manly s alpha Electivity Score (α) was used (Manly et al. 1993). The calculation of Manly Alpha index for habitat characterisation requires data on utilisation (f r ) and habitat availability (g r ). α r = f r g r n R f i j =1 g i 59

82 The usage was measured as the number of schools of a particular species (t) found in each habitat (r), while availability was defined as the proportion of the overall study area (A) covered by each habitat type. Alpha values vary from 0 if the taxon of interest completely avoids the habitat to 1 if there is an absolute preference for a particular habitat. If each habitat type is used in proportion to its availability, all r are equal (Pledger et al. 2007). Data were pooled by region and within region by species, to test the null hypothesis that habitat was selected randomly in proportion to its availability (H0: r = 1 n R ), against the alternative hypotheses of habitat preference or avoidance (Ha: r 1 n R ). The Bray-Curtis Index of dissimilarities (BCD) (Bray and Curtis 1957), was used as the test statistic, and statistical significance was tested using a randomisation procedure, which compares the observed usage value against the expected usage values within the study area (Manly 1997, Pledger et al. 2007). BCD = n R n r=1 f r E(f r ) n = R r=1 f r E(f r ) R r=1 (f r + E(f r )) 2F Expected usage values were estimated under assumption of H 0. Under this hypothesis the total number of schools sighted (F = n R r=1 f r ) should be subdivided per habitat according to its availability, therefore the expected number of schools per habitat r was estimated as E(f r ) = F g(r). Expected values were obtained by randomly assigning 1000 times the total number of schools, F, to each habitat type r with a probability g(r) (Pledger et al. 2007). BCD varies between 0, for completely matching vectors, to 1 for the most extreme dissimilarity. Statistical significance of any departure from random distribution was tested by counting the number of times pseudo-bcd values exceeded the observed value. Because a significant difference via the Bray Curtis index was found, a two-sided randomisation test was used to test which of the habitats was used more or less often than expected. The difference (Dr) of r estimate from 1 n R (expected distribution under H 0 ) was used as the test statistic. P-values were determined estimating the proportion of pseudo D r values, obtained with 1000 randomisations, further away from 0 than the D r value obtained from observed data. In this test, preference or avoidance for the selected habitat is indicated by significant positive or negative D r values respectively. To avoid type 1 error, Bonferroni correction for multiple comparisons was applied (Pledger et al. 2007). 60

83 Distance analysis To evaluate if dolphin distribution within each community home range is different from that expected by chance, a one tailed randomisation test was carried out following a method slightly modified from the Distance Analysis (DA) presented by Conner and Plowman (2001). The one-tailed (or simple) randomisation test (Manly 1997) was used to compare the mean values of distances to a particular habitat from the observed snubfin (µ s ) and humpback (µ h ) dolphins locations, with mean values obtained from random locations (µ r ) within the community home range. This procedure was repeated 10,000 times, and the significance of the test was evaluated by recording the number of times the mean value from random locations was greater than the observed value (H 0 : µ s - µ r 0, H 0 : µ h - µ r 0) (Manly 1997). Resample and randomisation tests were run using POPTOOLS version 2.5. Two DAs were conducted: one with real data and one with random data. A number of random locations equal to the total number of observed locations were generated inside each community home range, so that only locations that could be used by dolphins were used in the analyses. For each dolphin and random location, three distance values were measured: 1) distance to land, 2) distance to the closer of any among the following microhabitats: a) intertidal flats, b) sandbanks, c) rock reefs and 3) distance to the deeper waters. Distance to intertidal flats, rock reefs and sandbanks were estimated calculating the distance between each sightings and the 0 m contour depth. Distance to deeper waters was estimated as the distance from each sighting to the closer among any of the 10, 20 or 30 m isobaths. To estimate distances spatial layers for the three environmental variables described above were constructed. The distance of sightings to each spatial layer was estimated using the Euclidian Distance function available in Hawth Tools, ArcGIS 9.3 (ESRI) to compute the shortest distance to the nearest spatial layer. 4.3 Results Survey effort As outlined in Chapter 3, between January 2006 and October 2008, a total of 20,248 km of transects were surveyed in core study areas, during approximately 1,760 hours of survey time. However, of these only 1,255 hours were in a sea state < 3. Of these hours were spent surveying Keppel Bay, hours surveying Port Curtis and 189 hours in the Northern Region. During this period, a total of 437 humpback dolphin schools (PC = 189, NR = 63, KB = 185) were sighted in the three core areas and 179 schools of snubfin dolphins 61

84 were sighted in Keppel Bay study area. Additionally 4 schools of humpback dolphins and 1 of snubfin dolphins were sighted in transition areas. Of these only 304 (69%) humpback dolphin schools (PC = 115, NR = 46, KB = 143) and 87 (48%) snubfin dolphin schools were suitable for social structure analysis (i.e., including schools with all marked dolphins photo-identified). As humpback and snubfin dolphins tend to avoid vessels, a lower number of schools (HD: N TOT = 298, KB = 122, NR = 45, PC = 154; SD = 130) were associated with an accurate geographic position and were used in home range and habitat use analyses. Furthermore, as water depth and bottom structure can vary over short distances within the study areas, and this variation is not always visible from the surface, it was necessary to obtain broader geographical positions to limit habitat misclassification Social structure of humpback and snubfin dolphins The number of identified adult humpback dolphins included in the association analyses, with a minimum of 1, 2, 3, and 4 sightings, decreased from 161 (entire catalogue of adults marked individuals) to 109 (67% of the all catalogued animals), 84 (52%) and 62 (38%) respectively. Similarly for snubfin dolphins the number of identified dolphins included in the association analysis varied from 54 (entire catalogue) to 52 (96%), 44 (81%) and 30 (55%) corresponding to 1, 2, 3 and 4 sightings. The permutation test for both species was stabilised after 10,000 permutations at P values always over the significance level, indicating the absence of preferred or avoided companionship (Tables 4.1 and 4.2). For both species the data set including individual dolphins with at least three sighting records was chosen for association analysis. Association index values for the selected dataset varied from 0 to 1 but were generally low (HD: mean HWI = 0.09, SD = 0.16; SD: mean HWI = 0.18, SD = 0.17) with only 34% of the total possible associations recorded for humpback dolphins and up to 68% for snubfin dolphins. Assignment of individuals to clusters using the Hierarchical Cluster Analysis Modularity 1 supports the subdivision of humpback dolphins into three communities (Q maxima = 0.56). All humpback dolphins preliminarily associated to a particular area were correctly grouped together (Fig. 4.1). Overall mean association index values among humpback dolphins groups were significantly smaller than within groups (Table 4.3). In contrast, for snubfin dolphins, the modularity index was below 0.3 (Q = 0.12) which indicates the absence of a population substructure (Newman 2004) (Fig 4.2). 62

85 The same results were obtained by applying the Girvan and Newman method. Before applying the cutting off technique, all of the 84 humpback dolphins observed three times during the study period were linked in one network with 2364 edges, while after the permutation technique the individuals were linked with 2342 edges. The humpback dolphins network was best described as being composed of three communities (first maxima: Q = 0.420) (Fig 4.3). Almost all the 84 individuals were grouped following the region of provenence with the exception of two dolphins that were initially classified to the KB region but were more strongly associated with individuals from the Northern Region. Despite the lower level of association among communities than within community (Table 4.3), interaction among individuals of different communities does exist indicating that humpback dolphins along the Capricorn Coast form a unique population subdivided into three subpopulations or communities. In contrast, the 44 snubfin dolphins identified more than three times were linked in one large network with 1298 edges, which remained unvaried after the permutation technique (Fig. 4.4). The snubfin dolphins network was best described as being composed of a single community with Q values for n 2 subgroups always just above 0. Table 4.1. Summary of association pattern analysis of humpback dolphins for each selection criteria. In the table NS = number of sightings, n = number of individuals in the analysis, Q = Modularity Index, HWI = Half Weight Index, SD = standard deviation, Real = Mean HWI for observed data, Random = Mean HWI for permuted data, % = percentage of observed associations for real and random data, P = significance value. N of dolphins per database Mean HWI for the entire dataset Mean HWI for observed associations Criteria n Q Real (SD) Random(SD) % Real/Random (SD) P NS>1 n = (0.15) 0.07 (0.15) 21/ (0.17)/0.31(0.17) 0.73 NS>2 n = (0.16) 0.08 (0.15) 29/ (0.16)/0.28(0.15) 0.30 NS>3 n = (0.16) 0.10 (0.16) 34/ (0.16)/0.27 (0.15) 0.63 NS>4 n = (0.16) 0.11 (0.15) 39/ (0.15)/0.26 (0.14)

86 Table 4.2. Summary of association pattern analysis of snubfin dolphins for each selection criteria. In the table NS = number of sightings, n = number of individuals in the analysis, Q = Modularity Index, HWI = Half Weight Index, SD = standard deviation, Real = Mean HWI for observed data, Random = Mean HWI for permuted data, % = percentage of observed associations from real and random data, P = significance value. N of dolphins per database Mean HWI for entire dataset Mean HWI for observed associations Criteria n Q Real (SD) Random (SD) % Real/Random (SD) P NS>1 n = (0.16) 0.14 (0.16) 52/ (0.13)/0.27 (0.13) 0.18 NS>2 n = (0.16) 0.17 (0.16) 64/ (0.13)/0.26(0.13) 0.16 NS>3 n = (0.17) 0.18(0.16) 68/ (0.13)/0.27 (0.15) 0.19 NS>4 n = (0.17) 0.22 (0.17) 80/ (0.14)/0.28(0.14) 0.30 Table 4.3. Summary of association pattern analysis of humpback dolphins among communities. In the table NS = number of sightings, n = number of individuals in the analysis, Q = Modularity Index, HWI = Half Weight Index, SD = standard deviation, Real = Mean HWI for observed data, Random = Mean HWI for permuted data, % = percentage of observed associations for real and random data, P = significance value. N of dolphins Mean HWI for entire dataset Mean HWI for observed associations Criteria n Real (SD) Random % Real/Random (SD) P (SD) KB-PC (0.05) 0.01 (0.05) 11/ (0.05)/0.14 (0.05) 0.18 KB-NR (0.02) (0.01) 2/ (0.04)/0.14(0.03) 0.62 PC-NR <0.01 (0.0005) 0.01 (0.0005) 0.4/ (0.005)/0.13 (0.01)

87 Figure 4.1. Dendrogram showing average linkage cluster analysis of 84 humpback dolphins using mean sighting locations and HWI values. Individual dolphins are represented by codes based on the preliminary community classification on the left of the figure. Clusters of dolphins are joined by vertical lines. The different colours indicate the three clusters or communities: Keppel Bay (red), Port Curtis (green) and Northern Region (Blue). 65

88 Figure 4.2. Dendrogram showing average linkage cluster analysis of 44 snubfin dolphins using mean sighting locations and HWI values. Individual dolphins are represented by identification numbers. Clusters of dolphins are joined by vertical lines. 66

89 Figure 4.3 The social network of the humpback dolphins population presented by applying the Girvan Newman algorithm on association data after the cut-off permutation technique. Dolphin identification codes are shown in white squares with expected community membership and identification number. Three communities are evident: Northern Region (yellow), Keppel Bay (blue) and Port Curtis (red). Figure 4.4.The social network of the snubfin dolphins population presented by applying the Girvan Newman algorithm on association data after the cut-off permutation technique. Dolphin identification codes are shown in white squares with their identification number. 67

90 4.3.3 Home range There was no spatial overlap among the home ranges of the three humpback dolphin communities distinguished in the association pattern analysis (4.3.1). The three distinct areas covered about 269 km 2, 608 km 2 and 140 km 2 of the Northern Region, Keppel Bay and Port Curtis core areas respectively. (Fig 4.5). The absence of geographical overlap among communities home ranges indicates that sightings of dolphins outside their community home range are rare and likely to occur only occasionally. Furthermore the fact that the home ranges are not continuous toward the extremities, indicates that dolphin sightings in these areas become uncommon. Whereas the discontinuity in the representative ranges of humpback dolphins does not demonstrate the presence of three distinct populations, as interactions of individuals among different communities were observed, it indicates that dolphins belonging to a particular community are more strongly associated to a particular area. In contrast, the snubfin dolphins showed a single continuous representative range although limited to the south-west of the Keppel Bay core area (Fig. 4.6). The total area used by snubfin dolphins with at least three sighting records was estimated to be about 471 km 2. As for humpback dolphins, the snubfin dolphin representative range is formed by a main hull and multiple smaller hulls, which indicates that the sightings decrease when moving further away from the core area. 68

91 Figure 4.5. Representative ranges (95% kernel range) for the three humpback dolphin communities distinguished by association patterns in the Capricorn Coast region. 69

92 Figure 4.6. Representative ranges (95% kernel range) for snubfin dolphins in the Capricorn Coast region. 70

93 4.3.4 Habitat selection The use availability model for all individuals detected, based on 10,000 randomisations, showed significant differences between habitat use and availability for both humpback and snubfin dolphins (SD: BCD = 0.21, SD = 0.03, P < 0.01; HD-KB: BCD = 0.13, SD = 0.05, P < 0.01; HD-PC: BCD = 0.17 SD= 0.07 P < 0.01). The Dr tests (Table 4.4, figure 4.7) for selection or avoidance of each macro-habitat showed that humpback dolphins in Keppel Bay used shallow interior (2-5 m) and shallow subtidal habitats (5-10) (P < 0.01) more frequently than expected by chance. Intertidal habitats (0-2) were used in accordance to their availability (P > 0.005). The three deeper subtidal habitats were used less frequently than expected by random chance (P < 0.01). In Port Curtis (Table 4.4, Fig. 4.8) humpback dolphins used intertidal habitats and deeper water categories 1 and 2 (10-15 m and m) less frequently than what expected by chance (p < 0.003), shallow interior in accordance to their availability (P > 0.05), whereas shallow subtidal habitats were used more frequently than expected by chance. The Dr tests for snubfin dolphins (Table 4.4, Fig. 4.9) suggest that intertidal habitats and deeper waters categories 2 and 3 were used less frequently than what was expected by chance (P < 0.001), whereas shallow interior and shallow subtidal habitats 1 and 2 were used significantly more frequently than what was expected under a random distribution (P < 0.01). Deeper water category 1 was used in accordance to its availability (P > 0.05). Table 4.4 Habitat use availability analysis for humpback and snubfin dolphins. Category = depth range in m at lowest astronomical tide, Av = proportion of microhabitat availability; n= number of detected dolphins; a r = Manly s alpha index; Dr= deviation between a r and 1/n R. There were no areas deeper than 20 m in the Port Curtis area. Habitat Humpback dolphin KB Snubfin dolphins KB Humpback dolphins PC Category Av n α r Dr Av n α r Dr Av n α r Dr NA NA NA NA 71

94 Figure 4.7 Map of Keppel Bay showing the different habitat types defined based on water depth, and including sightings of humpback dolphins (red circles) used in the analysis of habitat preference and distance analysis. 72

95 Figure 4.8 Map of Port Curtis showing the different habitat types defined based on water depth, and including sightings of humpback dolphins (red circles) used in the analysis of habitat preference and distance analysis. 73

96 Figure 4.9 Map of Keppel Bay showing the different habitat types defined based on water depth, and including sighting of snubfin dolphins (red circles) used in the analysis of habitat preference and distance analysis 74

97 4.3.5 Distance analysis, distribution of dolphins in relation to land, intertidal habitats and deeper waters A summary of distances to land intertidal habitats and to deeper waters is given in Table 4.5. The randomisation test indicated that in the Port Curtis region, humpback dolphins do not occur closer or further away in relation to intertidal habitats than expected under a random distribution. Whereas humpback dolphins occur significantly closer to land and further away from deeper waters than expected by chance. In Keppel Bay the randomisation test indicated that humpback dolphins do occur closer to land and intertidal areas than expected by chance, while the distance to deeper waters was not significant. Snubfin dolphin locations in relation to the land and intertidal habitats and deep waters was not significantly different from a random distribution of sightings. 75

98 Table 4.5 Overall mean, mean of the 95% CI and ranges for distance to land, to intertidal areas and deeper waters of humpback and snubfin dolphins sightings in Keppel Bay and Port Curtis study area and for random locations. Values are in kilometres unless specified for meters (m). Specie/ Area Distance to land Distance to intertidal habitats Distance to deeper waters Mean (SD) 95%CI Range Mean (SD) 95%CI Range Mean 95%CI Range Sc PC 1.1 (0.86) 66.9(m) (m) (0.45) 11.8 (m) (m) (0.36) 6.85(m) (m)-2.42 Sc KB 2.8 (4.21) (m) m(6.4) (4.41) 52.2 (m) (m) Oh KB 3.7 (2.53) (0.68) (0.68) Ran KB 4.4 (5.85) (m) (0.79) 86 (m) (7.90) Ran PC 0.5 (0.54) (m) (0.39) (m) (0.46) (m)-2.01 Table 4.6. Effect sizes (μ-μ r ), 95% confidence intervals, followed by corresponding P-values from randomisation tests for distance to land, distance to intertidal area, and distance to deeper waters. In the table species: S,c, = Sousa chinensis and O.h. = Orcaella heinsohni; Area: PC = Port Curtis and KB = Keppel Bay. Species Area Distance to land Distance to intertidal habitats Distance to deeper waters μ-μ r 95%CI P μ-μ r 95%CI P μ-μ r 95%CI P S.c. PC S.c. KB O.h. KB

99 4.4 Discussion Limitations As discussed in Chapter 3, the low survey effort along the transition areas and Northern Region compared to Keppel Bay and Port Curtis as a result of weather constraints and limited accessibility is a potential bias in this analysis. In this analysis, interpretation of social structure, space use and habitat preferences may be biased toward the core study areas where most of the sampling was done. Nonetheless, transition areas were determined based on habitat preference for both humpback and snubfin dolphins in Queensland waters, and the lack of sightings in these areas suggests that neither species spend much time in these areas. The small number of resightings per individual and the uneven survey effort among areas and years are further potential sources of bias to consider. The number of dolphins sighted in a different area to the one of origin, increased through the study, with most of the mixed schools sighted in the last year when exceptionally good weather conditions allowed a substantially larger number of surveys to be completed. This coincided also with an extensive trawling activity recorded in the winter of 2008 in Keppel Bay. Dolphins from surrounding communities, may have immigrated temporarily into the area, attracted by the easy to locate and abundant food source Social and spatial structure During this study, a total of 304 schools of humpback and 87 schools of snubfin dolphins met the criteria (i.e. all adult marked dolphins in the school were identified) established for the analysis of social structure. In particular for snubfin dolphins, the number of schools suitable for the analysis of social structure was only 42% of the entire dataset. The smaller number of schools in which the entire number of adult individuals was photo-identified was the result of the dolphin avoidance behaviour. On several occasions, both humpback and snubfin dolphins disappeared after being sighted, particularly when a calf was present or when there was only a single individual present. Fine-scale, intra-population structure can exist even in the absence of physiographic barriers to movement. The Capricorn Coast is a relatively large heterogeneous environment with a few physical obstacles to the movement of individual dolphins. 77

100 Both techniques used in this study to define the social structure of humpback and snubfin dolphins in the Capricorn Coast yielded very similar results in terms of overall structure, with humpback dolphins subdivided into three communities, while snubfin dolphins were grouped into a single population. For humpback dolphins the null hypothesis of the existence of a single community was rejected. Analysis of association pattern and home range indicated that considerable structure exists in the ranging patterns and social interactions of humpback dolphins inhabiting this region. Along the Capricorn Coast humpback dolphins are separated into three social units, with completely geographically separated ranges coinciding with the Northern Region, Keppel Bay and Port Curtis study areas. Almost all the individuals that were initially categorised with the location of origin (where they were first sighted) were correctly grouped together with individuals identified in the same location, with only two exceptions. These were the two dolphins recorded the first time in Keppel Bay that were more strongly associated with dolphins from the Northern Region. Similar to Wells (1978), who described adjacent communities of dolphins in warmer waters, the home ranges of the three communities did not overlap. It could be argued that this finding of community structure reflects the existence of a series of overlapping individual ranging patterns without true community boundaries. Under this scenario, the finding of significant differences in the geographic ranges of putative communities is only an artifact of a series of overlapping individual ranges. However, this hypothesis does not explain the significant differences in patterns of association among communities. In addition, we would not expect the existence of distinct groups in the cluster analysis if communities of dolphins did not exist. We conclude that at least three communities exist in the Capricorn region. This structure fits the original definition of community structure (Wells 1978) which assumed that such communities exist in allopatry, compared to more recent studies, showing distinct communities with considerable overlap in ranging patterns (Chilvers and Corkeron 2001, Lusseau et al. 2005, Parra 2006) The evolution of a fine-scale population structure in a population may be promoted by various ecological factors including competition between social groups for habitat and food resources, predation risks, mating strategies and the distribution of food resources (Wilson et al. 1997, Connor et al. 1998). Prey resources may occur predictably in space and time, allowing individual dolphins to develop foraging strategies that are adapted to prevailing local conditions (Rossbach and Herzing 1999). In such areas, foraging 78

101 strategies may be culturally transmitted along matrilines (Mann and Sargeant 2003, Krützen et al. 2004), further promoting the benefits of philopatry. In this study the few instances where individuals from different communities were in close proximity did not involve any aggressive behaviour or a change in behaviour. Different feeding strategies or competition within or between community members were not observed. Analysis of habitat preferences did not show a substantial discrepancy in the way humpback dolphins used each of the study areas, with areas ranging from 2-5 m and 5-10 m the preferred habitats. Differences in distance to the land, intertidal habitats, and deeper waters cannot be compared among study areas, as they are likely to be related to the structure of the habitat more than species specific factors. Habitat fragmentation, distance and predation risk are together likely to be the main causes of the fine-scale population structure recorded in the Capricorn Coast. As no dolphins were observed in transition areas and resightings of individuals occurred more often in the same core area, it is likely that movement of individuals among areas is low, and is limited by the higher risk of predation along open coastline and by the long distances between core areas. Evidence in support of this hypothesis is the higher association pattern recorded among communities closer to each other compared to communities further apart. Dolphins moved more often between Port Curtis and Keppel Bay study areas, located only 30 km apart and separated by a small shallow channel with limited predation risk, than between Keppel Bay and the Northern Region, located more than 60 km apart and separated by deeper coastal waters known to support large numbers of sharks (pers. communications from local fishermen). It is important to note that communities are not closed demographic units, because genetic exchange may occur across boundaries (Wells 1986, Duffield and Wells 1991, Wells et al. 1998). Furthermore, individuals may occasionally change community membership over time. Such changes in community membership also occur in some chimpanzee societies (Goodall 1986). Movement of dolphins between regions may be triggered by vagrant individuals or occasional high and easily accessible food resources, such as during trawling activity in coastal areas (Connor et al. 1998), because it is easier for the animals to exploit a concentrated food source (Fertl and Leatherwood 1997). As described in Chapter 3, large schools of humpback dolphins including dolphins from different core areas were sighted feeding behind trawlers in Keppel Bay between April and June

102 Contrary to the pattern observed for humpback dolphins, snubfin dolphins in the Capricorn Coast form a single population with a range restricted to the southern end of Keppel Bay. This result also contrast with other recent studies, in which a fine-scale population structure was detected among individuals sharing the same area. For example, bottlenose dolphins (Tursiops aduncus) in Moreton Bay, Queensland, Australia occur in two sympatric communities that differ in foraging behaviour and social associations (Chilvers and Corkeron 2001). The two bottlenose dolphin communities shared an overlapping core area of 7.7 km 2, which comprised 31% and 14%, respectively, of the two communities core areas. In the Moray Firth, Scotland, two communities of bottlenose dolphins (Tursiops truncatus) were also separated by patterns of association, but overlapped in their ranging patterns (Lusseau et al. 2005). As suggested in Chapter 3 the snubfin dolphin population appears to be geographically isolated, therefore interaction is limited to individuals within the population. The small population size (see Chapter 7) spread over a relatively large range did not promote the development of a fine-scale population structure. In Shark Bay, Western Australia, a fine scale population structure was found in a large bottlenose dolphin population living in a relatively uniform environment (Krützen et al. 2004). This fine scale population structure has been related to the high dolphin density and the need to develop different feeding strategies and alliances to exploit difference resources and habitats (Krützen et al. 2005). The Fitzroy River in Keppel Bay is one of the largest catchments in Australia (see Chapter 3). Therefore this large area may provide enough food resources for all the individuals without the need to exploit different resources and develop different feeding strategies. Based on this preliminary information, snubfin dolphins in Keppel Bay seem to fit the definition of Evolutionary Significant Unit as outlined in Chapter 1, rather than Management Units (Palsboll et al. 2007). Dizon et al. (1994) characterised populations with the highest probability of being an ESU as having a discontinuous genetic divergence pattern where a locally adapted and closely related population is geographically separated; this increases the probability that habitat differences exist between isolated populations, resulting in different selection pressures. Cleary more detailed genetic studies are needed to clarify the conservation status of the Keppel Bay snubfin dolphin population. 80

103 Chapter 5 Population genetics of Australian snubfin and humpback dolphins In this chapter, I examined the population genetic structure and gene flow between geographically separated neighbour populations of humpback and snubfin dolphins from Keppel Bay, Port Curtis, and the Northern and Southern Great Sandy Strait using mitochondrial DNA control region and nuclear microsatellite markers. 81

104 5.1 Introduction Conservation genetics and importance to conservation biology Understanding the evolutionary role of gene flow is pivotal for the conservation of endangered populations and the development of appropriate conservation strategies for declining species (Storfer 1999). In the past, management strategies for threatened species have focused primarily on protecting declining populations and on maintaining areas of natural habitat in an attempt to alleviate decreases in population numbers due to demographic and/or environmental stochasticity (Marcot 1992, Richards et al. 1993). For cetacean populations, perhaps the most essential and immediate application of genetic analyses is the identification of which groups should be recognised as distinct entities (stocks, community or sub-population), followed by study of their demography and ecology. Populations that occur within contiguous habitats such as in the marine environment are expected to follow an isolation-by-distance model, where the distance between populations is the overriding factor contributing to genetic differentiation (Slatkin 1993, Dixon et al. 2006). However, recent studies on cetaceans, fishes and some marine invertebrates in open ocean regions and around oceanic islands have revealed unpredicted genetic subdivision at very small geographic scales (Mӧller et al. 2007). This differentiation can be augmented by processes such as habitat fragmentation. Habitat fragmentation occurring through the creation of dispersal barriers can reduce gene flow and lead to isolation of populations (Hitchings and Beebee 1998, Dixon et al. 2006), and to a reduction in population size, which may cause a decrease in genetic variation (Frankham et al. 2002). Genetic variation within a species needs to be conserved to guarantee the long term survival of animal populations. Small, relatively isolated populations are more vulnerable to genetic stochasticity, such as inbreeding and genetic drift, which causes loss of genetic variation and affects population viability via factors such as susceptibility to disease and a decline in fitness associated with increased homozygosity (Newman and Pilson 1997, Saccheri et al. 1998). Therefore, loss of genetic diversity and genetic factors associated with small population size can greatly contribute to increase a population s risk of extinction. 82

105 5.1.2 Genetic approach to identify management units During the last two decades the role of genetics in conservation biology and in ecology in general, has increased greatly (Frankham 1995, Frankham et al. 2002). One of the more important developments in the field of conservation genetics has been to move away from quantifying genetic diversity at a species level, and to recognise that genetic diversity needs to also be documented at a population level (Moritz 1994). For conservation purposes it is important to understand how genetic diversity is partitioned spatially among populations, i.e. to determine population genetic structure. Describing population genetic structure enables us to make inferences about levels and patterns of dispersal among populations, the potential for diversification and differentiation among populations, and the evolutionary history of populations (Avise 1994, Bossart and Prowell 1998). It is also important to understand the ecological and evolutionary processes that may have shaped the patterns of population structure (Moritz 1994) The partitioning of populations into smaller, isolated or semi-isolated units can have an important bearing on many demographic and evolutionary processes, therefore the identification of such units is important for implementing appropriate conservation and management procedures at a local level (Secchi et al. 2003). The need to identify units below the level of species that should be prioritized for protection (Frasier and Bernatchez 2001) has led to the identification of two main definitions of conservation units: the Evolutionary Significant Unit (ESU) and the Management Unit (MU) (Moritz 1994). The ESU identifies historically isolated populations, monophyletic in respect of mitochondrial DNA (MtDNA) control region and with significant genetic differentiation of loci. The goal of designating these distinct units is to allow their separate management so as to retain the long-term evolutionary potential of the species (Firestone et al. 1999). MUs, on the other hand, are based on significant allele frequency differences regardless of the phylogenetic relationships of the alleles. The concept of MUs was introduced to emphasize the need of protection for those populations connected by such low levels of gene flow that they are functionally independent. These units are therefore more appropriate for short-term local conservation goals such as population monitoring (Firestone et al. 1999). To define ESUs and MUs, the examination of both nuclear and mitochondrial loci are fundamental. Neutral genetic markers are now widely used to describe the population structure of a wide range of species; in particular mitochondrial DNA (mtdna) and microsatellite loci are the most extensively used markers in population genetic structure 83

106 studies. MtDNA is a circular haploid molecule, which is inherited only maternally (Wilson et al. 1985). Within the MtDNA, the most variable part is the Control Region (or d-loop), while the mutation rate is much smaller in the coding parts. The control region of MtDNA has therefore become one of the most common genetic markers used to resolve phylogenetic relationships between closely related taxa. Microsatellite loci are short, highly variable regions of DNA with tandemly repeated short sequences. Repeats motifs range between one to five base pairs. Such loci are often highly polymorphic, characterised by multiple alleles and high heterozygosity. These properties make them the first choice for examining the fine scale population structure, and they are relatively cheap to analyse allowing large scale studies. The criterion suggested by Moritz (1994) to identify MUs was not without criticisms. The criterion use to identify MUs is not well defined because neither the type nor the number of loci used in searches for allele frequency differences were specified (Paetkau 1999). Furthermore, the power of genetic analyses to detect population structure depends on the variability and numbers of markers, the sample size of individuals and the sampling locations (Paetkau 1999). Nevertheless, if results are analysed with caution these criterion can still provide a useful and easy to apply approach to identify distinct management units. This is particularly important when the species being studied, such as the case of humpback and snubfin dolphins and their large marine environment, render the sample collection a huge task for researchers with inevitable sampling limitations and consequent data issues Conservation in cetaceans Some cetaceans have a wide distribution in the world s oceans; some species are highly mobile, and able to migrate over large distances, such as between ocean basins, whereas other species show high phylopatry to a particular area despite the absence of physical barriers (Möller et al. 2007). As a result, cetacean species exhibit a complex pattern of population genetic structure at both species and population levels (Hoelzel 1998). Bottlenose dolphins (Tursiops spp.) provide the best example to show this extreme heterogeneity. Bottlenose dolphins show not only a high degree of genetic differentiation among geographically neighbouring populations without the presence of obvious physical boundaries, but also a high level of genetic exchange between distant ocean basins (Krützen et al. 2004; Möller and Beheregaray 2004; Quérouil et al. 2007). 84

107 At the present there is no information on population structure of Australian snubfin and humpback dolphins. However, we know that most of their habitat in Central and southern Queensland has been substantially modified by human activities (Parra et al. 2005, Cagnazzi et al. 2011). Important habitat has been affected through urbanisation and industrialisation in coastal areas, and overexploitation of coastal resources. The consequences of these activities include the fragmentation of natural habitat (Briggs and Walters 1997). Loss of habitat is often associated with an increasing geographic and genetic isolation among populations, making each of them more vulnerable to random genetic processes, which can lead to a reduction of the effective population size, genetic variability, and of their evolutionary potential (Bijlsma et al. 2000, Hedrick 1994, Spielman et al. 2004). Humpback and snubfin dolphins appear to populate the Queensland coast in geographically discrete populations (see Chapter 3 for more details). However, knowledge about the connectedness of the discrete populations is lacking. Because dolphins can move for relatively large distances, some interchange between populations is expected. Movements of some humpback dolphins between separated populations were documented in this study (see Chapters 3-4) through the analysis of photo-identification data. The majority of these movements were recorded between Keppel Bay and Port Curtis and to a much lesser extent between Port Curtis or Keppel Bay and the Northern Region. Similar results were obtained by the author during a study completed in the Great Sandy Strait (Cagnazzi et al. 2011). Photo-identification data distinguished two populations with very low interaction; one found in the Northern Great Sandy Strait (NGSS) and the second in the Southern Great Sandy Strait (SGSS). Though important, information that can be taken from photo-identification data is limited by several human and environmental limiting factors. Therefore a genetic approach is needed to clarify the population structure and movement patterns of humpback and snubfin dolphins in Queensland, Australia. The primary aims of this study are to determine the level of genetic diversity and migration rate among four neighbouring populations of humpback dolphins (Keppel Bay, Port Curtis, Northern Great Sandy Strait and Southern Great Sandy Strait) along the Central and Southern Queensland coast, and among snubfin dolphins from Keppel Bay in Central Queensland, and snubfin dolphins from Cleveland and Halifax Bay, Northern Queensland. 85

108 5.2 Data Collection Procedures for sample collection Between 2007 and 2008, a total of 71 tissue samples of humpback dolphins and 11 samples of snubfin dolphins were collected. Samples of humpback dolphins were collected from 4 geographically separate neighbouring populations along the Queensland coast: Northern Great Sandy Strait (NGSS) and Southern Great Sandy Strait (SGSS) in Southern Queensland (see Cagnazzi et al. 2011), and Port Curtis and Keppel Bay along the Capricorn Coast in Central Queensland (Fig. 5.1). Attempts to collect samples from dolphins in the Northern Region study area were unsuccessful. Samples from 9 snubfin dolphins were collected only from Keppel Bay, as this was the only population found in the study area. Microsatellite data from thirty samples of snubfin dolphins collected from 3 regions in northern Queensland (Halifax Bay, Cleveland Bay and Hinchbrook Channel) were provided by Dr Guido Parra to allow comparison with the Keppel Bay population (Fig. 5.2). To minimise the risk of re-sampling the same individual or close relatives, boat based surveys were conducted following the same procedures outlined in Chapter 3 for Gladstone, Keppel Bay and Northern Region, and in Cagnazzi et al. (2011) for the Great Sandy Strait. Samples were collected using the PAXARMS system (Krützen et al. 2002), a biopsy system developed to collect skin samples from small cetaceans. This system consists of a modified 0.22 calibre rifle with a larger barrel to fit the biopsy darts that are made out of polycarbonate with stainless steel biopsy tips. Biopsy heads have a diameter of 5 mm and a length of 8 mm to keep the amount of material collected to the minimum size suitable for genetic study, and to guarantee dolphin welfare. Three barbs are punched into the walls of the cutting head to aid sample retention after the dart hits the tissues. A valve fitted to the chamber on one side of the rifle allows for controlling pressure and the power of the shot, allowing the researcher to modify the pressure based on the distance of the dolphin from the boat. The rifle is loaded with blank charge. More detailed information and a complete design of this system can be found in Krützen et al. (2002). 86

109 Figure 5.1. Geographic distribution of the 71 biopsy samples of humpback dolphins collected between 2008 and

110 Figure 5.2. Geographic distribution (red areas) of the 41 samples of snubfin dolphins used in the analysis. 88

111 Sampling was carried out only in calm sea conditions. After a school of dolphins was sighted, it was approached up to a distance of about 100 m, in order to maintain visual contact without alarming the dolphins. Dolphins were then approached at a very slow speed, avoiding variation in propeller speed, up to a distance of about 50 m. Sighting and photo-identification data were collected by two trained volunteers, while the author was on the stern loading the PAXARMS biopsy system. For safety reasons the rifle was charged only on the bow of the boat with the volunteers standing behind the console. The boat was then aligned almost parallel to the dolphin s course, slightly drifting toward the pod, with the aim to approach the school to about 30 m. Once within sampling distance, darting was attempted only if no boats or people were in visual proximity, there were no calves present, dolphins showed a predictable behaviour, and no dolphin was likely to surface in line between the rifle and the target animal. Samples were collected only from adult sized individuals that were not obviously old or showing any sign of recent injury. The target location is a circular area slightly behind the dorsal fin (Fig. 5.3). If sampling was successful the dart holding the sample was retrieved from the water using a net. The school was then followed for about 5 minutes to collect data on dolphin reaction to biopsy sampling. The sample was extracted from the biopsy head using plastic gloves and sterilised tweezers, and it was preserved in 80% ethanol. Each tube containing one sample was labelled with date, time, sampling area, pod n and sample n. At the end of the field work the rifle and darts were dissembled, cleaned and sterilised following the protocol described in Krützen et al. (2002). Each cutting head was sharpened and the holding teeth aligned. Each skin sample was catalogued with the identification number of the dolphin sampled and was then stored in the fridge until analysed. This research was conducted under the Southern Cross University Animal Care and Ethics Committee approval n 10/13 and the Great Barrier Reef Marine Park and Queensland Marine Park scientific research permit n G.10/

112 Figure 5.3. Picture taken during a successful attempt showing the dart in red near the top of the picture, just before it hits the dolphin. 5.3 Material and methods: DNA extraction and amplification DNA purification and extraction Total DNA was extracted from approximately 5-10 mg of tissue following the instruction provided with the DNA purification protocol of mouse tail tissues in the Gentra Puregene Kit (QIAGEN). This protocol requires the following steps: 1. Cut a small piece, if possible including skin and blubber from the sample and add the tissue sample into a 2 ml tube with 300 µl of Cell Lysis Solution. 2. To disrupt the tissue, add a metal bead to the tube and put the tube into a tissuelyser for one minute at 20 Hz. 3. Add 1.5 µl of Puregene Proteinase K to disrupted tissue. 4. Seal the tube with parafilm foils and incubate at 55 C in an overhead rotator overnight. 5. Add 100 µl of Protein Precipitation Solution and vortex it for 20 sec at high speed. 6. Centrifuge for 3 minutes at 13,000 g to separate the supernatant from undigested material. 7. Add the supernatant from the previous step into a new tube with 300 µl of isopropanol and mix it by inverting 50 times. 8. Centrifuge to 13,000 g, discharge the supernatant and drain the tube by inverting it onto an absorbent paper. 90

113 9. Add 300 µl of ethanol to wash DNA pellet and follow the same directive in point 7 (pellets should remain on the bottom of the tube). Dry the pellet. 10. Add 50 µl of DNA Hydration Solution and vortex it. 11. Incubate at 65 for one hour to dissolve DNA and then at room temperature overnight. DNA concentration was tested with NanoDrop 1000 (Thermo Scientific), a fluorospectrometer that enables analysis of extremely small sample volumes (1-2 µl). Following fluorospectrometer results the original extracts were diluted to a DNA concentration of 20 ηg/µl Genetic sexing The sex of each individual was determined by amplifying via the polymerase chain reaction (PCR) fragments of the X and Y chromosome using ZFX and SRY primers and following the protocol described in Gilson et al. (1998) (see Appendix 1 for PCR protocol) Mitochondrial DNA screening and sequencing A DNA fragment of approximately 460 base pairs, comprising the proline transfer RNA gene and parts of the hypervariable region I of the control region, was amplified for 71 different individuals, using the polymerase chain reaction. PCR set up consists of 1µl of diluted template, 0.4 µl of Dlp-1.5 (5'-TCACCCAAAGCTGRARTTCTA-3') and Dlp-5 (5 - CCATCGWGATGTCTTATTTAAGRGGAA-3' ) at 10 µm (Baker et al. 1993), 0.4 µl of dntps 10 mm, 0.25 µl of MgCl 2 25 mm, 2 µl of buffer, 0.05 µl of Taq polymerase and 15.5 µl of doubled distilled water for a final reaction volume of 20 µl (Appendix 2a). PCR conditions consisted of an initial denaturation at 94 C/1 min, followed first by a touchdown cycle with annealing temperature decreasing of 1 per cycle from 63 C to 55 C (1 min) repeated for 9 cycles and final extension at 72 C/1 min. A cycle of 94 C (30s), 52 C (30s) and 72 C(1 min) was repeated 29 times and completed with a final extension of 10 min at 72 C (Appendix 2b). PCR products were tested by gel electrophoresis. Successful amplified products were cleaned using, QIAquick purification kit (Quiagen). One µl of purified products were amplified using BigDye Terminator v3.1 Cycle Sequencing Kit (Applied Biosystems) following manufacturer s instruction (Appendix 2b). PCRs were run on a Verity 96-Well Fast Thermal Cycler (Applied Biosystems). Sequencing fragments were detected on an ABI PRISM 3730 DNA Analyser. Sequences were edited using SEQUENCING ANALYSIS Software, version 5.2 (Applied Biosystems). Low quality 91

114 samples were re-amplified adding more or less template, increasing the number of PCR cycles, or using the reverse primer dlp Microsatellite genotyping Samples were genotyped at 27 microsatellite loci, previously used in many cetacean population structure studies (Appendix 3a). Eight different dinucleotide microsatellite loci were used: MK3, MK5, MK6, MK8, MK9 (Krützen et al. 2001), EV37 (Valsecchi and Amos 1996); KWM12 (Hoelzel et al. 1998); and D22 (Shinohara et al. 1997), and 19 tetranucleotide loci: 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, Tur4_142, Tur4-153, Tur4_162, Tur4_D8, Tur4_E12, Tur4_F10 (Nater et al. 2009) (Appendix 3a). The 27 primers were combined in three different primermix (Appendix 3b) and amplified in three multiplex PCR (MP1, MP2 and MP3). The reaction-mix for each multiplex was composed of 1 μl of template, 0.8 μl of primermix, 4 μl of Qiagen Multiplex Primermix and ddh2o. PCR protocols for each multiplex are described in Appendix 3c. One μl of 50 times diluted PCR product was added to 10 μl of Hi-Di formamide including 0.07 μl of GeneScan 500 LIZ size standard (Applied Biosystems) and was denaturated for 3min at 95 C. Allelic sizes were scored against the size standard and run on a ABI PRISM 3730 DNA Analyser. Results were analysed with the computer programme Gene-Mapper version 4 (Applied Biosystems). 5.4 Data analysis MtDNA data analysis Sequences were aligned using SeqMan software (DNAstar). Using Arlequin v.2.0 (Schneider et al. 2000), genetic diversity was summarised as the proportion of polymorphic sites, haplotype diversity (equivalent to expected heterozygosity, adjusted for population size), and nucleotide diversity (Nei 1987). Genetic distance between haplotypes was calculated using Tamura and Nei distance (Tamura and Nei 1993) with γ- correction of 0.5 in Arlequin Microsatellite data analysis Data quality control and genetic variability 92

115 Scoring error was evaluated by repeating amplification and scoring for 10% of samples at all loci and calculating the frequency of disagreement between runs. All samples were tested for possible duplicates (i.e. dolphins sampled more than once), using Microsatellite Toolkit (Park 2001). Samples with 100% genotypic identity were considered duplicates and removed. Samples were further tested using CERVUS (Marshall et al. 1998) to estimate the probability of identity (P(ID)) for two individuals with similar genotypes. Pairs of samples with similar allele frequency but high probabilities of identity were also eliminated. All microsatellite genotypes for the remaining samples were screened for the presence of irregular repeat unit length, large allele dropout and null alleles using MICRO-CHECKER (Van Oosterhout et al. 2004). The presence of null alleles can be indicated by higher than expected level of homozygosity indicated by high values of the inbreeding coefficients, F IS (Weir and Cockerham 1984), which was calculated for each population at each locus using GENEPOP 4 (Raymond and Rousset 1995). Departure from Hardy-Weinberg equilibrium was tested in the program Arlequin 3.1 (Excoffier et al. 2005), for each population at each locus using Fisher s exact test with 10,000 dememorisation and 100,000 Markov chain steps (Guo and Thompson 1992). To test if microsatellite loci were independently inherited, genotypic linkage disequilibrium was tested in GENEPOP. P-values were estimated using a Markov chain method with 1,000 dememorisation steps, 100 batches, and 1,000 iterations per batch (Raymond and Rousset 1995). Because of multiple comparisons, Bonferroni corrections were applied to the Hardy-Weinberg and linkage disequilibrium tests. Levels of genetic variability were evaluated by calculating the number of alleles, observed heterozygosity (Ho), and expected heterozygosity (He), using MS-tools. Allelic richness, an estimate of the number of alleles corrected by sample size (Petit et al. 1998), was calculated using the program FSTAT (Goudet 2001). Population structure and dispersal analysis Genetic differentiation was investigated using both F ST statistic, based on the infinite alleles model (IAM) (Weir and Cockerham 1984) and R ST statistic based on the step-wise mutation model (SMM) (Slatkin 1995). While F ST index takes into account allele frequencies, R ST takes into account both allele frequencies and genetic distance (Slatkin 1995). F ST estimations can be potentially depressed by high mutation rates such as those 93

116 found in many microsatellite loci (Balloux and Lugon-Moulin 2002) whereas R ST is less dependent upon mutation rates (Balloux et al. 2000). In humpback dolphins the analysis of molecular variance, AMOVA, was used to examine the partitioning of genetic variation within and among the geographic regions, Capricorn Coast (KB and GLD) and Great Sandy Strait (NGSS and SGSS) and within and among populations (KB, GLD, NGSS and SGSS). In snubfin dolphins, genetic difference was tested only among populations, from Keppel Bay, Cleveland Bay (CLB) and Halifax Bay (HLB). The programme Arlequin was used to estimate both F ST and R ST statistics and to run the AMOVA. Differentiation among pairs of populations was further investigated with the population comparison module in ARLEQUIN for both F ST and R ST values. Gene flow between pairs of localities (Nm) was estimated from F ST, R ST using the private allele method as described in Slatkin (1995) that does not assume a model of population structure (Slatkin and Barton 1989). Population structure was further investigated using a Bayesian algorithm implemented in STRUCTURE (Pritchard et al. 2000). Structure was used to probabilistically group individuals into K populations based upon their genotypes. For both species, population structure was first investigated using admixture model which considers that each individual draws some fraction of the genome from each of the K populations. This model was first run without a priori information on sampling locality. Because of the low level of polymorphism within humpback and snubfin dolphins, analyses were also run including information on sampling sites to improve the accuracy of assigning individuals to clusters and improving the overall estimate of the model fit (Pritchard et al. 2000). To identify the best number of putative populations (K), that explains the observed genetic differentiation, the admixture model was first run with only 100,000 iterations and initial burn-in of 10,000. The number K was increased until posterior probability, lnpr(x K), started decreasing constantly. The final structure was assessed running the model for 1,000,000 iterations with an initial burn-in of 100,000 for K values varying from 1 to the value that maximised the posterior probability. Five independent tests were run to test the consistency of the results. 94

117 5.5 Results Humpback dolphin population structure Sample collection and data screening During 2007 and 2008 a total of 71 biopsy samples of humpback dolphins were collected from 4 geographically distinct sites: 22 samples in Keppel Bay, 15 in Gladstone, 13 in the NGSS and 21 in the SGSS. No samples were collected from the Northern Region. Overall sample size is small but still represents a large proportion of the individuals that can be sampled (Cagnazzi et al. 2011, and Chapter 4). Results from identity analysis using microsatellite polymorphic loci and mtdna control region revealed the presence of four full matching pairs. One of each of the duplicate samples was removed from the dataset. A further test in Cervus did not identify any other exact match, but did highlight 4 fuzzy matches i.e. two genotypes with only one different allele. Given that the P(ID) for full-siblings was low [P(ID)<0.001] and that both pairs of samples showed identical mtdna control region haplotypes and were of the same sex, these samples were considered duplicates and only one of each pair was included for further analysis. After the exclusion of these samples, a dataset with 65 unique individuals (KB = 21, PC = 13, NGSS = 12, SGSS = 19) was used for population structure analysis. Analysis of mitochondrial control region sequences Sequence analysis of 461 base pairs of the MtDNA control region revealed 9 variable sites, defining 4 unique haplotypes (Table 5.1). Only one haplotype (HAP-A) occurred in all four geographic sites, and this haplotype was also the most common. Overall, haplotype and nucleotide diversity were very low. Within populations, haplotype diversity varied from 0 in SGSS and NGSS where only one haplotype was found, to 0.57 (±0.06) in KB and 0.58 (±0.09) in PC. Nucleotide diversity varied from 0 in the SGSS and NGSS to (±0.004) and (±0.004) in KB and PC respectively. Considering this low genetic variation within and between sampling regions the mtdna was not used as a marker to assess genetic differentiation at the population level. 95

118 Table 5.1 Polymorphic sites within mtdna control region sequences for each haplotype (H). Numbers indicate the position along the 461 base pairs long fragment. N = number of samples with the specific haplotype. Loc. = population where each different haplotype is found: PC (Port Curtis), KB (Keppel Bay), NGSS (Northern Great Sandy Strait), SGSS (Southern Great Sandy Strait). H N Loc A 49 KB-PC-NGSS-SGSS T T A C T G T T C C 7 KB-PC C C G C C G T T C D 1 PC C C G T C A C C A E 8 KB C C G T C G T T C Analysis of microsatellite data Of the 27 microsatellite loci amplified in Sousa chinensis samples, 17 were polymorphic (Table 5.2). The 10% quality control analysis revealed less than 0.3% rate of disagreement between initial and secondary scores. Microsatellite data were tested in Micro-Checker, and no evidence of null alleles, allele dropout or scoring error emerged. In Cervus, null allele frequency was almost always below the acceptance level of 10%, with the exception of loci: KWM12, MK8 and MK9. As F IS values for MK8 and MK9 per population were negative or not significantly positive the presence of null alleles in these loci was considered insignificant (Table 5.3). Heterozygosity excess was detected (P value for the global test for all loci and all populations = 0.34±0.0015) only for the SGSS- KWM12 pairwise comparison. Hardy Weinberg equilibrium probability found only 1 locus population comparisons, KWM12 vs. SGSS, to be significantly out of Hardy Weinberg equilibrium even after Bonferroni correction for multiple comparisons (Table 5.3). After each pair of loci was tested across all sites and within populations for linkage disequilibrium, no loci resulted consistently in linkage disequilibrium across all sites, and no populations were consistently in linkage disequilibrium across all loci. Based on these results locus KWM12 was excluded from the analysis. Overall genetic diversity did not vary significantly between populations (Mean Ho = 0.37 SD = 0.18; ANOVA: F = 2.08 p = 0.11). The maximum number of alleles per locus was 5, but the overall mean was below 3 alleles per locus (Mean = 2. 46, SD = 0.24). The number of private alleles was higher in the NGSS with 4 private alleles, compared with the 96

119 two recorded in the other regions. Average allelic richness, estimated over 12 samples per region, was similar to the overall average number of alleles and varied from 2.02 in the SGSS (SD = 0.4) to 2.58 in the NGSS (SD = 0.71) (Table 5.2). Table 5.2 Genetic variation at each microsatellite locus for each population. The numbers of individuals analysed for each population are indicated below the population names. The allelic richness (AR.), heterozygosity observed (Ho), and heterozygosity expected (He) are reported. Populations Keppel Bay (21) Port Curtis (13) NGSS (12) SGSS (19) Loci Ho He AR Ho He AR Ho He AR Ho He AR E EV MK MK MK MK T_ T_ T_ T_ T_ T_ T_ T_ T_ T_

120 Table 5.3. Test results for Hardy-Weinberg equilibrium including P-values and F IS values for all loci and population comparisons. Significance level (P) after Bonferroni correction was Significant P values are shown in bold. Keppel Bay Gladstone NGSS SGSS Locus P F IS P F IS P F IS P F IS E EV KWM MK MK MK MK TUR4_ TUR4_ TUR4_ NA NA TUR4_ TUR4_ TUR4_ TUR4_ TUR4_ TUR4_ TUR4_ Population genetic structure Genetic differentiation among samples from Central and Southern Queensland was highly significant using both genetic distance methods, F ST and R ST (AMOVA, F CT (F ST )= 0.08 d.f. = 1 P = and F CT (R ST ) = 0.18 d.f. =1 P < 0.001) and accounted for 8.7% and 18.3% of the total variance respectively. Most of the genetic variation detected in the samples was explained by differences within populations (Table 5.4). A small but significant part of molecular variance was also explained by differences among populations within regions (Table 5.4). Genetic differentiation remained highly significant also among the 4 putative populations (AMOVA, F ST = 0.12, d.f. = 129 overall P < and R ST = 0.23 d.f. = 129 P < Table 5.5). 98

121 Table 5.4. Variance components (var) and permutation probabilities for AMOVAs. Dataset was partitioned according to regions (Capricorn Coast and Great Sandy Strait). df = degrees of freedom, SSD = sum of squares, permutation probability is given for the probability that randomised value > observed value. Results for both evolution models are presented. Source of variation Df SSD var % var F P Distance method: number of different alleles (F ST ) Among regions (F CT ) Among populations within region (F SC ) Within populations (F ST ) <0.001 Total Distance method: sum of squared size difference (R ST ) Among regions (F CT ) Among populations within <0.001 region (F SC ) Within populations (F ST ) Total Table 5.5. Variance components and permutation probabilities for AMOVAs when dataset was partitioned according to populations (KB, GLD, NGSS, SGSS). df = degrees of freedom, SSD = sum of squares, permutation probability is given for the probability that randomised value > observed value. Results are present for both evolution models. Source of variation df Sum of squares Variance component Percentage of variation Permutation Probability Among populations Within populations Total Among populations Within population Total

122 Significant differentiation was found in almost all of the possible pairwise comparisons excluding the comparison between Keppel Bay and Gladstone using R ST (Table 5.6). Estimated migration rate between the Capricorn Coast and the Great Sandy Strait was low and varied from 1 (F ST = 0.11, P < 0.01, Nm = 1.9) and 2 (R ST = 0.18, P < 0.01, Nm = 1.05) individuals per generation depending on the method used. Estimates of migration rates (Nm) between the four original populations obtained from F ST and R ST varied slightly. Overall gene flow remained low, with localities geographically closer to each other showing higher values of Nm (Table 5.6). However analysis of genetic similarity vs. geographical distance was not consistent with a pattern of isolation by distance (Mantel tests derived from F ST : r = 0.82, P = 0.08). Table 5.6 On the lower triangular matrix genetic differentiation among pairwise humpback dolphin populations using F ST values are reported, while R ST values are reported in the upper diagonal (*P < 0.05, **P < ). Nm values (shown in parentheses) are reported underneath F ST and R ST values. Comparisons Keppel Bay Port Curtis NGSS SGSS Keppel Bay \ (7.5) ** (1.13) 0.275** (0.65) Gladstone 0.048** (4.87) \ * (2.01) 0.252** (0.73) NGSS 0.129** (1.67) 0.104** (2.13) \ 0.115** (1.91) SGSS 0.159** (1.32) 0.166** (1.24) 0.094** (2.4) \ In the preliminary test run in STRUCTURE the highest posterior probability (lnpr(x K)) was obtained for K = 4 and then slightly declined for larger K values. In the final analysis K values were varied from 1 to 4. After five runs for each K value lnpr(x K) peaked at K = 3, while the lowest lnpr(x K) was reached for K = 1 (Table 5.7). According to the probabilistic assignment of individuals, genetic structure was apparent for the 3 clusters. Almost all the samples from Keppel Bay and Gladstone were grouped in a single cluster, apart from one individual that was assigned a greater probability of belonging to the NGSS cluster. NGSS and SGSS samples were primarily assigned to respective populations with only 2 and 1 samples respectively showing higher probability of belonging to a different cluster (Fig. 5.4a). For K = 2, all samples but one were correctly assigned to their regional population 100

123 being either Capricorn Coast (KB and PC) or Great Sandy Strait (SGSS and NGSS) (Fig 5.4b). With the inclusion of information on sampling location, the overall quality of the model increased but results changed slightly. Posterior probability was maximised at K = 3, while the lowest lnpr(x K) was reached for K = 1, while the model with 4 possible populations was ranked second (Table 5.7). Table 5.7 Estimated posterior probabilities for K that varies from 1 to 4, based on results from the Admixture model without/with information on sampling location, and % of membership for each predefined population in the inferred clusters. The consistency of the results was tested through 5 independent runs for each K number of tested populations. Only results that maximised lnpr(x/k) for each K value are presented in the table. K Admixture model % of membership for the 4 predefined populations lnpr(x/k) KB (1) PC (2) NGSS (3) SGSS (4) / / /3 20.0/ / / / / / / / / / / / /1.5 19/ / / / / / / / / / / / / / / / /3.3 9/ / / / / / /

124 a) b) Figure 5.4 Structure plots showing estimated proportions of the coefficient of admixture of each individual s genome that originated from K population, for K = 3 (a) and k = 2 (b) without prior information on dolphin location. Each individual is represented by a column. Geographical origin of the samples is given below the graphic. The numbers 1 4 indicate the sampling location being KB, PC, NGSS and SGSS respectively Snubfin dolphin population analysis Identity analysis and dataset delineation During 2007 and 2008 a total of 9 samples from snubfin dolphins were collected in the Keppel Bay study area. Microsatellite data from 30 samples of snubfin dolphins, obtained using the same protocol applied in this study, and collected from three locations in north Queensland (Cleveland Bay = 11, Halifax Bay = 17, Hinchinbrook Channel = 2) were provided by Dr Guido Parra, Flinders University, Adelaide. Of the 27 microsatellite loci genotyped, 25 amplified, of which only 12 were polymorphic. The 10% quality control analysis revealed no disagreement between initial and secondary scores in the 9 samples from Keppel Bay. MStools revealed the presence of only one identical match. After this duplicate sample had been removed, the software Cervus did not identify any other exact match or a fuzzy match. A dataset including a total of 38 samples and 12 polymorphic loci was used in the following analyses. 102

125 Analysis of mitochondrial control region sequences MtDNA sequences were available only for samples collected in Keppel Bay. Sequence analysis of 472 base pairs of the mtdna control region revealed 4 variable sites, defining 2 unique haplotypes. No further analysis was done on MtDNA at this stage due to the small sample size. Data quality control There was no evidence for null alleles, allele dropout or scoring error. Linkage disequilibrium was detected in 3 loci comparisons. After Bonferroni correction was applied, only one pair of loci was in linkage disequilibrium across all populations (Tur4_117 vs. Tur4_153). Significant departure from Hardy Weinberg equilibrium was found only for locus EV37 in the Halifax Bay population, however some loci were monomorphic in one population, hence in those cases the HW Test could not be applied (Table 5.8). Considering the small number of polymorphic loci and that these are only preliminary results, analyses were run using all the available data. Genetic variability Overall genetic diversity was very low (Table 5.9) and after removing the data from the only two samples collected from the Hinchinbrook Channel, it did not vary significantly between populations (Mean Ho = 0.35 SD = 0.02; ANOVA: F = 0.96 p = 0.39). The maximum number of alleles per locus was 7, but the overall mean was below 3 alleles per locus (Mean = 2. 65, SD = 0.51). In the Halifax and Keppel Bay populations, 3 and 2 private alleles were found, respectively. Average allele richness was also not significantly different among populations (AR = 1.9, SD = 0.6, ANOVA F = 0.3 P = 0.8). 103

126 Table 5.8: Test results for Hardy-Weinberg equilibrium including P and F IS values for all loci and populations. M indicates monomorphic loci for a particular population. Significant p value after Bonferroni correction was Cleveland Bay Halifax Bay Hinchinbrook Keppel Bay Locus P F IS P F IS P Fis P F IS E EV37 M M M na M na KWM M na M na MK M na M na MK M na M na TUR4_ TUR4_ M na TUR4_142 m m M na TUR4_ M na TUR4_ TUR4_ TUR4_ Table 5.9 Genetic variation at each microsatellite locus for each population. The numbers of individuals analysed for each population are indicated below the population names. The allelic richness (AR), heterozygosity observed (Ho), and heterozygosity expected (He) are reported. Pop Cleveland Bay Halifax Bay Hinchinbrook Keppel Bay Loci Ho He AR Ho He AR Ho He AR Ho He AR E EV37 na na 0 1 na 0 1 KWM na 0 1 MK na MK na 0 1 na 0 1 T_ T_ T_ T_ T_ T_ T_

127 Population genetic structure Because only two samples were available from Hinchinbrook Channel these were excluded from this analysis. Therefore population differentiation was tested only among Cleveland Bay, Halifax Bay and Keppel Bay. Overall differentiation among populations was highly significant using both F ST and R ST and accounted for 11% and 12% of the total variance respectively (Table 5.10). Most of the genetic variation in the samples was explained by differences within populations (Table 5.10). Significant differentiation was found only between all the possible pairwise comparisons including Keppel Bay populations (F ST (KB-CB) = 0.22, P < 0.01; F ST (KB-HB) = 0.18, P < 0.01; F ST (HB-CB) = -0.01, P = 0.77). Results using R ST coincide almost exactly with results presented above (Table 5.11). Estimates of migration rates (Nm) between the Halifax and Cleveland Bay population toward Keppel Bay were below one individual per generation regardless of the method used (Table 5.11). Using the admixture model without information on sampling location, STRUCTURE was not able to distinguish among groups, probably due to the low number of polymorphic loci and overall genetic diversity of the loci analysed. Including data on sampling location, lnpr(x K) declined sharply beyond K = 3. In the second analysis posterior probabilities were estimated for K values from 1 to 3. LnPr(X/K) peaked at K = 2 (LnPr(X/K) = ) and declined from K = 3 (Table 5.12). Geographic structure was apparent for two clusters, with one cluster assigned completely to the samples from Halifax, Cleveland Bay and Hinchbrook Channel, while the second was entirely assigned to Keppel Bay (Fig. 5.3) 105

128 Table 5.10 Variance components and permutation probabilities for AMOVAs when the dataset was partitioned according to populations (KB, CB, HB). df = degrees of freedom, SSD = sum of squares, permutation probability is given for the probability that randomised value > observed value. Results are presented for both evolution models. Source of variation df SSD var % var F P Distance method: number of different alleles (F ST ) Among populations Within populations Total Distance method: sum of squared size difference (R ST ) Among populations <0.001 Within populations Total Table 5.11 On the lower triangular matrix genetic differentiations among pairwise populations using F ST values are reported, while R ST values are reported in the upper diagonal (*P < 0.05, **P < ). Relative estimates of Nm are reported in parentheses underneath each F ST and R ST values. The negative F ST values among Cleveland Bay and Halifax Bay is an artefact of the method used which arise when there is no differentiation among populations. Comparisons Keppel Bay Cleveland Bay Halifax Bay Keppel Bay \ Cleveland Bay 0.221** (0.87) Halifax Bay 0.187** (1.05) 0.216** (0.9) \ (Infinite) 0.231** (0.8) (Infinite) \ 106

129 Table 5.12 Estimated posterior probabilities for K that varies from 1 to 3, based on results from the Admixture model without/with information on sampling location, and % of membership for each predefined population in the inferred clusters. The consistency of the results was tested through 5 independent runs for each K number of tested populations. Only Results that maximised lnpr(x/k) for each K are presented in this table. K Admixture model % of membership for the 4 predefined populations lnpr(x/k) HB (1) CB (2) HC (3) KB (4) 1-738/ / / / / / / / / / / / / / / / / / /2.8 35/ / / /89.3 Figure 5.5 Structure plot of the estimated proportions of the coefficient of admixture of each individual s genome that originated from K = 2 populations, with prior information on dolphin location. Each individual is represented by a column. Geographical origin of the samples is given below the graphic. The numbers 1 4 indicate the sampling location being Halifax Bay, Cleveland Bay, Hinchbrook Channel and Keppel Bay, respectively. 107

130 5.6 Discussion Data consideration This was the first attempt to examine the genetic variation and differentiation among populations of humpback and snubfin dolphins in Australia. These species are among the most difficult to sample due to their tendency of avoiding boats, their shy behaviour and small size. These samples will also be used in a longer collaborative project including researchers from various Australian Universities, which aims to obtain samples from populations of these two species from their entire Australian coastal ranges. The present study covers a large geographic area and includes samples from 4 geographically distinct sites and therefore these results provide an initial valuable insight into the population structure of Australian snubfin and humpback dolphins in Australian waters. It is important to note that the sample size was low if compared to studies on relatively abundant and easily sampled species such as bottlenose dolphins, but is similar to other studies on less abundant or uncommon species such as Franciscana dolphin (Pontoporia blainvillei), Dusky dolphin (Lagenorhynchus obscures) or spotted dolphin (Stenella attenuata) (Harlin et al. 2003, Lazaro et al. 2004, Escorza-Trevino et al. 2005). Furthermore, the number of samples collected from both species and for each population represents a large proportion, varying from a minimum of 20% up to about 33% of the total adult population size. Nevertheless, the small sample sizes for some regions could potentially introduce bias into these estimates of genetic divergence and the statistical power to detect differences in the genetic variability among regions is low (Taylor and Gerrodette 1993). However, it is important to note that small sample sizes generally tend to cause population differences to be statistically non significant (Waples 1998), resulting in an underestimate of the degree of population structuring Within population levels of genetic diversity Both humpback and snubfin dolphins exhibited very low levels of haplotype and nucleotide diversity compared with other dolphin species (HD: h = , π = ; SD: h = 0.50, π = 0.004). In New Zealand for example despite restricted migration, significant population structure, and relatively small population sizes, all populations of coastal bottlenose dolphins (Tursiops truncatus) showed substantially higher haplotype 108

131 and nucleotide diversity (h = 0.91, π = 0.02) (Tezanos Pinto et al. 2009). Overall in the present study haplotype and nucleotide diversity was substantially smaller compared to a large population of about 3000 inshore bottlenose dolphins (Tursiops aduncus) from Shark Bay, Western Australia (h = 0.66, π = 0.02) (Krützen et al. 2004), and Franciscana dolphins (Pontoporia blainvillei) from Argentina (h = , π = ) (Mendez et al. 2008). The only small cetacean populations studied to date that exhibit less genetic diversity are the North Island population of Hector s dolphins (Cephalorhynchus hectori) in New Zealand, which currently consists of only a single matriline (Pichler and Baker 2000), and the critically endangered vaquita (Phocoena sinus; see (Rojas-Bracho et al. 2006). Low genetic diversity was also detected in the 27 microsatellite loci screened in this study, with only 17 and 12 polymorphic loci for humpback and snubfin dolphins respectively. Furthermore the overall mean observed heterozygosity was less than 0.40 for both species. The microsatellite loci screened in this study were previously used on several cetacean species and were highly polymorphic (Valsecchi and Amos 1996, Krützen et al. 2001, Nater et al. 2009). In Shark Bay, Western Australia, more than 8 alleles per locus were found in bottlenose dolphins with an average Ho of 0.74 (Krützen et al. 2004). In bottlenose dolphins from the Bahamas an average of 5 alleles per locus were found and an Ho = 0.60 (Parsons et al. 2006), while in samples from bottlenose dolphins from eastern Australia the number of alleles per locus varied from 6 to 15 with Ho from 0.46 to 0.81 (Möller and Beheregaray 2001) Genetic differentiation and migration rate among humpback dolphin populations Results from this and previous studies (Parra et al. 2004, Cagnazzi et al. 2011) suggest that the contemporary distribution of humpback dolphins is discontinuous along most of the Central and Southern Queensland Coast. In this study, analysis of the distribution of mitochondrial and microsatellite genetic variance from samples of dolphins provides the first evidence for geographic structuring of humpback and snubfin dolphins populations in Australia. Throughout the geographic regions included in this study, humpback dolphins showed considerable genetic differentiation among populations. In fact, all putative populations defined by geographic regions showed significant differentiation from all other putative populations at microsatellite DNA loci. For humpback dolphins significant genetic differentiation was detected at both regional and population levels with the exception of 109

132 the Keppel Bay-Port Curtis pairwise comparison which indicated a higher degree of gene flow between these localities compared to the remaining sites comparisons. The self-assignment of individuals using a new model-based Bayesian algorithm further supports the higher level of gene flow between these latter two regions, with almost all samples collected in PC and KB grouping together in a single cluster, while almost all the samples from SGSS and NGSS were assigned to their original site. Migration rates estimated from F ST and R ST values varied slightly, with the exception of KB-PC, and less than two individuals per generation were estimated to move between all the possible sites comparisons. Photo-identification data also supports this pattern with more dolphins identified moving between KB and PC than between SGSS and NGSS (Cagnazzi et al. 2011, see Chapter 3) and all the remaining possible comparisons. As no other resident populations are found in the study area (Chapter 3), the overall population structure appears to be better explained by the separation of dolphins into three main management units: 1) Keppel Bay-Port Curtis, 2) Northern Great Sandy Strait, and 3) Southern Great Sandy Strait. Animal populations that occur within contiguous habitats are expected to follow an isolation-by-distance model, where the distance between populations is the overriding factor contributing to genetic differentiation (Slatkin 1993). However, these analyses suggest that a simple model of isolation by distance does not account for the genetic differentiation observed between sampling sites. In the Great Sandy Strait low levels of social interaction and high geographic separation was found between the Northern and Southern humpback dolphin communities (Cagnazzi et al. 2011). It was suggested that the infilling of the shallow channels linking the Northern and Southern Great Sandy Strait regions was the primary cause of the low level of interaction recorded among members of different communities (Cagnazzi et al. 2011). Similarly in this study significant genetic differentiation was found between the two communities. Significant genetic differentiation was found also between humpback dolphins from the Capricorn Coast and those from the Great Sandy Strait. No other population has been found between these two regions, and surveys for humpback dolphins conducted by the author from 2004 to 2006 resulted in no sightings (Author unpublished data). However humpback dolphins were once commonly seen by local fishermen in the Burnett River, located between Port Curtis and the Northern Great Sandy Strait. The Burnett River, once a large estuary, is now modified into a narrow deep channel with substantial changes in the flood system, water quality, catchment condition and hydrology (Norris et al. 2001). 110

133 River discharge has long been recognized as one of the factors that contribute to the high productivity of estuaries, which provides easy to access food to inshore dolphin species. These modifications have meant that passages for aquatic organisms have become restricted (DERM: The extent to which the sedimentation and infilling observed in the Great Sandy Strait and the modification of the Burnett River have affected the movement of humpback dolphins remains unknown. However these may be two examples of the consequences that habitat degradation and loss can have on the geographic and genetic isolation among populations. Therefore from a management perspective these results should be used to develop a more appropriate precautionary approach for managing coastal dolphins and their habitats. Significant structuring over relatively short geographical distances has also been documented for coastal Tursiops truncatus in other geographical locations. Populations of bottlenose dolphins along the Gulf of Mexico and southeastern coast of the United States were found to have significant differentiation of mtdna haplotypes (Dowling and Brown 1993, Natoli et al. 2004). In Sarasota Bay, Florida, long-term behavioural observations indicated considerable site fidelity (Wells et al. 1987, Wells 1991), and population structuring along the central west coast of Florida was supported by molecular data (Duffield and Wells 1991). Genetic structuring in T. aduncus-type bottlenose dolphin populations has also been documented on both the east and west coasts of Australia. On the east coast, Möller and Beheregaray (2004) have documented population differentiation between two sites separated by approximately 400 km. Significant population structuring was also found using both nuclear and mitochondrial markers for Shark Bay dolphins on the west coast over much shorter distances (Krutzen et al. 2004). The degree of subdivision described in this study for humpback dolphin populations is concordant with the genetic consequences of the fission-fusion social systems and site fidelity they express Genetic isolation of Keppel Bay snubfin dolphins The spatial genetic structuring of microsatellite genotypes revealed a marked genetic differentiation between snubfin dolphins from Northern Queensland (Cleveland Bay, Halifax Bay and Hinchinbrook Channel) compared with samples from Keppel Bay, in the Capricorn Coast, Central Queensland. In contrast, no genetic differentiation was detected among North Queensland snubfin dolphin populations. 111

134 While it must be acknowledged that these results are based on a limited sample size, and low genetic diversity with very few informative loci, the available sighting data (Chapter 3 and 7) support the genetic results. Nevertheless, the level of genetic differentiation at nuclear loci was detected over a large geographic distance (ca km); therefore because of incomplete sampling of individuals from regions in between these areas we cannot completely exclude the possibility that a simple model of isolation by distance would explain the genetic differentiation. Between Cleveland Bay and Keppel Bay snubfin dolphins are also known to occur in the Whitsundays and in Repulse Bay (see Chapter 7). Therefore, additional sampling from these areas is needed to provide sufficient resolution to answer this question. Given these preliminary results, snubfin dolphin population structure may indicate the presence of a separate panmictic population in North Queensland, whereas dolphins from Keppel Bay may be part of a relict population, established as result of exploratory movements of discrete schools of snubfin dolphins in search of alternative suitable habitats. These results are similar to those recorded for the North East Scotland bottlenose dolphin population, where photo-identification and genetic studies based on relatively small numbers of observations or samples (n = 29) concluded that currently, the NE Scotland population is both demographically and geographically isolated (Parsons et al. 2002). As noted above, the preliminary genetic data presented here are not conclusive and are based on small sample sizes. While it is acknowledged that the small sample sizes available for this study limit the power of AMOVA, the sample size effects would likely result in a failure to detect population structure. Furthermore, the strong pairwise differentiation between dolphins from North and Central Queensland resulted in the assignment of two distinct clusters in the STRUCTURE analysis, which supports the population subdivision detected, and suggests demographic independence of the Keppel Bay snubfin dolphin population from those in northern Queensland. The coast of Queensland is characterized by relatively continuous habitat suitable for inshore dolphins. Although exploratory surveys (Cagnazzi unpublished data) have documented a relatively low occurrence outside the study area, it is likely that some dispersal events are undetected, considering the mobility and ranging patterns of this species. However, high dispersal potential does not necessarily translate into high levels of realized gene flow, even among highly vagile large vertebrates (Paetkau et al. 1995, Avise 1998). Despite a high potential for long distance movements, significant population structuring has been detected for several species of small cetaceans. This is particularly 112

135 true for species that exhibit social structures characterized by stable, long-lasting affiliations like snubfin dolphins. Nevertheless, further sampling is therefore needed to clarify the population structure and to assess if snubfin dolphins in the Capricorn Coast are genetically isolated from conspecifics elsewhere. Sampling is currently underway in both Keppel Bay to increase the sample size and Repulse Bay to conduct comparative studies. 113

136 Chapter 6 Population estimates of Indo- Pacific humpback dolphins in the Capricorn Coast region In this chapter mark-recapture open population models were applied to estimate the abundance of Indo-Pacific humpback dolphins in the coastal waters of the Capricorn Section of the Great Barrier Reef Marine Park. Population estimates are provided for each core area, and for the overall region and within regions per capture occasion. 114

137 6.1 Introduction Top predators are often rare, subject to anthropogenic mortality, and possess life-history traits that make them inherently vulnerable to extinction (Williams and Thomas 2009). Over the last few decades of endangered species research, it has become clear that abundance, as difficult as it can be to estimate (Taylor et al. 2000), is the first basic information needed for managing animal populations (Cooke 1995). Population size and trends are used by government departments, such as the Department of the Environment, Water, Heritage and the Arts (DEWHA), and conservation agencies, such as International Union for Conservation of Nature (IUCN), for listing a species, population or ecological community of particular conservation concern, under one of the threatened categories. Estimating abundance is more challenging for rare species than for common species; the smaller the population, the harder it is to estimate its abundance (Thompson 2004). The difficulty in obtaining baseline data does not bode well for the conservation and management of many marine species, particularly coastal cetacean populations, whose size is often small and whose conservation status is unfavourable. Such is the case for the Indo-Pacific humpback dolphin, Sousa chinensis, (hereafter humpback dolphin) in Australia (Perrin 1999, Read and Wade 2000, Sinha 2002, Rojas-Bracho et al. 2006). In Australia, the status of humpback dolphins remains unknown due to limited data. However, at a meeting organised by the Queensland Department of Environment and Resource Management for the Back on Track species prioritisation framework, an expert panel suggested that this species could be listed as vulnerable under the Environmental Protection and Biodiversity Conservation Act 1999 (EPBC Act 1999). This status was considered the most appropriate at this time, in view of small population sizes, coupled with a high level of incidental catches, a decline in the number of sightings and the inexorable increasing urbanisation and industrialisation of coastal areas. Assessing the global status of a widely distributed species requires extensive time, funding and research effort. A quicker, but still important step parallel to this process is the assessment of the species status at a regional scale. For species such as coastal dolphins that experience varied anthropogenic pressures throughout their range, the recognition of such Management Units (MUs) is fundamental to proper short-term management, while waiting for enough information to clarify the status of that species at a National level. 115

138 Accordingly, the main aim of this chapter was to generate the most accurate initial abundance estimate of humpback dolphins in coastal waters of the Capricorn Coast region. In this chapter the abundance of mature individuals is also estimated as this parameter is used by the IUCN to include geographically isolated subpopulations in the IUCN Red List, following the assessment criteria for regional populations and subpopulations. 6.2 Methods Survey procedures and data collection See Chapter 3 for details on survey procedures, including maps of the study area, survey effort, data collection and definitions Data selection Photographs taken during this study were divided, based on their quality, into three categories: 1) photographs suitable for identification and photo-id matching, 2) photographs suitable for pod composition analysis, and 3) photographs not suitable for analysis. The quality of each picture was determined using three criteria: 1) position of the dorsal fin compared to the water surface, 2) light exposure, and 3) quality of the focus. The first category included only those photographs in which the dorsal fin was perpendicular to the water surface, and all marks and patterns of spots were clearly discernible, as a result of good focus and light exposure. The picture was classified into the second category if one of the three criteria did not meet the requirements outlined above. If none of the criteria were met, the picture was included in the third category and was not used for analyses. Only photographs belonging to the first category were used for mark recapture analysis, and for all the analyses in this chapter. Dolphins were catalogued using primary marks and secondary marks. Primary marks were nicks, notches or malformations of the dorsal fin, while patterns of spots or scratches on the dorsal fin, and marks on other body parts were used as secondary marks to assist in photo-id matching when dorsal fins were similar. 116

139 For each catalogued dorsal fin, one of four age-classes, was applied: old adult, young adult, juvenile and calf. To define age classes, definitions similar to those provided in Parra et al. (2005) were used. Old adults were fully grown individuals (more than 2 m long) showing signs of adult age such whitening of the dorsal fin, rostrum and body extremities (Fig. 6.1c). Young adults were dark gray individuals, > 2 m in length (Fig. 6.1b). Juveniles were light gray individuals approximately 2/3 the length of an adult, usually swimming in association with an adult, but sometimes swimming independently (Fig. 6.1a). Calves were individuals varying from black to a gray skin colour, < 1/2 the length of an adult, in close association with an adult, and swimming regularly besides or slightly behind an adult. Catalogued dolphins were pooled in two distinct databases: 1) Adult individuals (A) and 2) Juvenile individuals (J). The first database included individuals classified as old and young adults with long lasting marks and the second database included juveniles with long lasting marks, calves were not included. Based on the available evidence which indicates that in humpback dolphins sexual maturity does not occur until a minimum length of at least 2 m (Jefferson, 2000) the two databases were used to estimate the population size of mature (M) and immature (I) individuals respectively. Length of the animals was estimated visually using either a calf (usually 1 m in length) or a old adult individual (more than 2 m in length) present in the school for comparison. Dorsal fins without any distinctive features were classified by age class as non-marked, and their proportional abundance was used when estimating total population size. A distinct database was kept for each core area, Port Curtis (PC), Keppel Bay (KB) and Northern Region (NR). a b c Figure 6.1. Photographs of humpback dolphins of different age-class. In these photographs, it is possible to discern the colour variation and difference in size used to distinguish among a) juveniles, b) young adults and c) older adults (from left to right). 117

140 6.2.3 Estimating population size The term population has been variously defined, and is often modified to fit a researcher s needs. For the purpose of this research, two categories of a population used by the IUCN Red List for the assessment criteria were used; regional population and subpopulation. The term regional population identifies the portion of the global population within the area being studied, which may comprise of one or more subpopulations. Therefore, in this study the regional population refers to the entire population of humpback dolphins living along the Capricorn Coast. The term subpopulation, which normally identifies geographically or otherwise distinct groups in the regional population between which there is little demographic or genetic exchange (Begon et al., 1996, Williams et al., 2002), was used as synonymous with the term community (Chapter 4), and therefore it was used to distinguish amongst the Port Curtis, Keppel Bay and Northern Region communities. As a result of uneven survey efforts, population size was estimated separately for the three core study areas (PC, KB and NR). To investigate whether each subpopulation was open or closed (to immigration, emigration, mortality, or birth) the discovery curve was plotted for each core area to analyse the cumulative number of identified individuals against the survey effort in hours. The discovery curve for each area was plotted for two datasets: 1) only adult marked individuals, and 2) all the identified individuals which includes juveniles. To obtain an adequate sample size and to guarantee that all dolphins were available to be photographed during any capture occasion, data from PC and KB sites were pooled into two seasons corresponding approximately to the dry or winter season and the wet or summer season, for a total of four capture occasions: (1) October 2006 to April 2007, (2) May to September 2007 (3) October 2007 to April 2008, and (4) May 2008 to September The resulting history file was composed of four capture occasions coinciding with the four sampling seasons. Due to the initial limited knowledge of the study area, the slow identification rate and slightly different survey design applied (see Chapter 3), surveys conducted until September 2006 were excluded from the population analysis to minimise variation in capture probabilities between sampling occasions. In the NR site, surveys were possible only in winter (Fig. 5.4); therefore data were pooled in sampling periods resulting in a history file composed of three capture occasions coinciding with the 2006, 2007 and 2008 winter seasons. 118

141 6.2.4 Population estimates using open population models Validation of model assumptions The basic assumptions of mark-recapture modelling are the following: 1) every animal has the same capture and survival probability (Burnham et al. 1987, Pollock et al. 1990, Williams et al. 2002), 2) no marks are lost or overlooked (Pollock et al. 1990, Williams et al. 2002), 3) no behavioural responses (Pradel 1993), 4) sampling is instantaneous relative to the interval (i to i+1), and all individuals are released immediately after sampling (Pollock et al. 1990, Williams et al. 2002), and 5) permanent emigration (Kendall 1997, Williams et al. 2002). Photo-identification technique assumptions 2, 3 and 4 are usually met and are amply discussed in several publications (e.g. Wilson et al. 1999, Parra et al. 2005). Assumptions 1 can be rephrased as problems that are often addressed in statistics: 1) pseudoreplication, observations are not independent, sample size is inflated and precision is low, and 2) heterogeneity or overdispersion, statistical tests include extra source of variation due to extra-binomial variation, caused by age-effects, heterogeneity and different behavioural responses. For the Cormack Jolly Seber and related models a GOF test is available in the program Release to test Assumptions 1 and 5. Release calculates three tests, formally defined as TEST 3 the Survival Rate Test, TEST 2 the Capture Rate Test, and TEST 1 Group Test. TEST 3 tests the assumption of lack of heterogeneity, and it deals with whether animals were seen again after they were released on first capture. TEST 2 tests the assumption of independence. It deals with animals known to have been alive between sample occasions i and i+1. Test 1 is a test of differences between groups, but is not particularly useful, and the output is usually ignored. The chi-square tests for each transition among sampling periods have the advantage that they are independent and additive. As such, TEST 2 and 3 can be pooled in a summary statistic (TEST2 + TEST3) whose final P value indicates if Assumption 1 has been met. Stated simply, if the pool test statistic Test 2+Test3 is not significant, then assumptions 1 and 5 are met (Burnham et al. 1987, Pollock et al. 1990). Additionally relatively high estimates of capture probabilities further indicate that assumption 1 and 5 are met. Estimating the population size of marked individuals Estimates of marked individuals are provided for each dataset using the Arnason s parameterization of the Jolly Seber model (Seber 1982, Schwarz and Arnason 1996). 119

142 This model provides abundance estimates while allowing entries (i.e., births, immigration) and losses (i.e., death, permanent emigration) in the population under study, and is suitable for long-term studies where the use of models assuming population closure is not reasonable. Firstly, as for comparative purpose with most studies, population estimates of marked individuals were provided for each core area (x) only for the dataset including adult marked individuals (A), resulting in a history file with 4 sampling occasions and one group. In this form, the Arnason s parameterization of the Jolly Seber model for t = 4 occasions, estimates a total of 11 parameters, including the marked population size (Ň A(X) ), t estimates of capture (p), t 1 estimates of survival probability (Φ), and t 1 probability of entry (PENT). Estimates of marked individuals within core areas per season (t) (Ň A(x) (t)) were also provided. Estimates were then provided for the mature and immature datasets using the Jolly-Seber model with 2 groups (G): mature (M) and immature (I). Groups were separated to limit bias caused by the different capture and survival probabilities of dolphins in different life stages (Assumption 1). In this form the full time dependent model estimates 22 parameters, 11 for each group as specified above, and includes two estimates of Ň G, one for each group. Marked population size was estimated within each core area (x), for each group, Ň G(X), (with G being either mature, M, or immature, I, groups) and for each group per season, Ň G(X) (t). To obtain the most accurate estimates, several reduced models were fitted to the data by reducing the number of parameters from the general model (Pollock et al. 1990).Because the variation inflation factor ĉ was less than 1, indicating no overdispersion and heterogeneity in the data, each model was ranked using the Akaike Information Criterion modified for small samples (AICc = ln RSS n + n+k n k 2). Following the parsimony principle, the model with the fewest number of parameters and that provided a good fit of the data, i.e. ΔAICc < 2 from the best model, was selected (Burnham and Anderson 1998). The parameters for each model were estimated using maximum likelihood estimation using the program POPAN-5 (Arnason et al. 1998) available in MARK. Total population size by region Estimates from mark-recapture data consider only a fraction of the entire population, underestimating the real population size, which includes also non-marked individuals. To produce estimates of overall abundance, estimates of the abundance of distinctive 120

143 individuals were re-scaled to include animals that were not distinctive. The proportion of marked individuals, θ, was estimated as the ratio between the cumulative number of dolphins identified and the total number of dolphins sighted during the study. Only sightings in which all the dolphins in the school were photographed with at least with one photograph of the first quality category were used to calculate θ. The total population size per core area (N T(X) ) was calculated by adjusting marked population estimates of adult individuals for the selected area (Ň A(X) ) with the relative proportion of marked individuals (θ (X)) calculated for that particular area taking in account each age of class, which includes also juvenile and calves: N T(X) = Ň A(X) / θ (X). Similarly, the total population size within core area per season, Sx was estimated as: Sx(t)= Ň A (x) (t)/θ(x) (t), where θ A(x)(t) is the proportion of marked individuals calculated using photographs taken in the selected area, during one of the four sampling occasions t. Standard errors for the total population size were derived from the variance of N given generally by Variance N = N 2 [(varň/ Ň 2 ) + (1 θ /nθ)] where N is the total number of animals from which θ was estimated (Williams et al. 1993, Chilvers and Corkeron 2003). Confidence intervals were adjusted consequently. Total population size for the 2 groups model was estimated as follows: N G(X) = Ň G(X) /θ G(X). Similarly the total population size within core area and group per season (S G ) was: S G(X) (t) = Ň G(X)(t)/θ G(X)(t). 121

144 The proportions of marked individuals for each group were estimated only from photographs of individuals belonging to the selected group (M or I) in the selected area (PC, KB and NR). Hence generally variance was estimated for each group as: Var N G = N(G) 2 VarŇ(G) Ň(G) θ(g) nθ(g) Capricorn Coast humpback dolphins population size Approximate estimates of the total population size and of the total number of mature (M) humpback dolphins in the Capricorn Coast (CC) coastal waters was derived by combining available abundance estimates from each separate core area. The total population size of humpback dolphins in the CC (N CC ) and its associated coefficient of variation (CV) were derived as: N CC = N NR + N KB + N PC SE(N CC ) = SE 2 N NR + SE 2 N KB +SE 2 N PC CV(N CC ) = SE (N SGBR ) N SGBR Similarly was calculated the Capricorn Coast mature population size (M CC ). 6.3 Results Photo-Identification data A total of 189 humpback dolphins including 28 juveniles with long-term permanent marks were unequivocally identified in the study region between 2006 and The number of dolphins identified in each core area was only a small fraction of the total dolphins catalogued (Table 6.1). Sixty-three individuals (33%) were identified at least once in Port Curtis, 84 (44%) in Keppel Bay, and 42 (22%) in the Northern Region. Few dolphins were identified in more than one region, 16 dolphins were sighted in both PC and KB, 2 in NR and KB, and only one dolphin was sighted in all three regions. Even if dolphins move between core areas, most of the catalogued dolphins showed a strong preference for a particular site, 42 and 37 dolphins were sighted at least once during each capture occasion in Keppel Bay and Port Curtis respectively, the number increases to 63 and 49 dolphins respectively if survey periods are considered (Fig. 6.3). Dolphins sighted in more 122

145 than one region showed a strong preference for a particular site (Fig. 6.2), with generally only one record of the dolphin occurring in a different site to where the dolphin was mostly seen. Table 6.1. Number of dolphins identified per study site and distinguished by dataset. In the Northern Region no juveniles showed long term marks therefore the total number of marked individuals coincides with the group M. Region Database N Port Curtis Entire dataset 63 Group A 54 Group J 9 Keppel Bay Entire dataset 84 Group A 65 Group j 19 Northern Region Entire dataset 42 Group A 42 Group J 0 123

146 N of seasons and years in which a dolphin was sighted PC KB PC KB PC KB NR PC KB PC KB PC KB PC KB PC KB PC KB PC KB PC KB PC KB PC KB PC KB PC KB PC KB KB NR KB NR Figure 6.2. Number of seasons (dark gray) and years (light gray) in which, the 21 humpback dolphins sighted in more than one core study area, were sighted in each area. Each of the 21 dolphins shows a stronger preference to a particular site, while only one record was obtained in a different area. On the horizontal axis PC, KB and NR defined the core study area in which a particular individual, identified with the catalogue number, was sighted. 124

147 N of dolphins KB PC NR Figure 6.3. Resighting pattern of humpback dolphins. In KB and PC sighting patterns of humpback dolphins are summarised by showing the number of dolphins (y axis) sighted in different combinations of capture occasions (at the maximum 4) and sampling periods (at the maximum 2) (x axis). In the NR only the number of years in which dolphins were sighted was used (z) Model selection In Port Curtis, Keppel Bay and the Northern Region, the cumulative number of identified adult humpback dolphins (i.e., rate of discovery) plotted together with the survey effort kept growing throughout the study, suggesting populations were open for the duration of the study (Fig. 6.4 and Fig. 6.5). Further tests to assess population closure were not necessary as the large extent of the study area, the coverage of only a fraction of the entire area, and the movement of some individuals between core sites excluded a priori the application of closed population models. Hence each population was considered to be demographically open for the duration of the study. The shape of the Keppel Bay discovery curve (Fig. 6.4) seemed to have reached a plateau in July 2007, before a steep increased occurred between April-May 2008 before a second plateau. This shape can be the result of immigration of discrete schools from the surrounding regions, or the return of resident individuals from areas outside the normal range, rather than individuals developing new marks. 125

148 In Port Curtis the discovery curve levelled off to an almost constant level, with 71% of the individuals showing permanent marks being catalogued by July 2007 (Fig. 6.4). The new dolphins added to the Port Curtis catalogue toward the end of the study were all temporary visitors from Keppel Bay. In the Northern Region the discovery curve followed a more standard pattern with a constant increase in the number of catalogued dolphins with time. However, new dolphins were added to the catalogue throughout the three years (Fig. 6.5). Figure 6.4. Discovery curves of the cumulative number of adult humpback dolphins (adults) and adults + juveniles humpback dolphins (all) identified between January 2006 and October 2008 in Keppel Bay (top), and Port Curtis (bottom). 126

149 Figure 6.5. Discovery curves of the cumulative number of humpback dolphins identified between January 2006 and October 2008 in the Northern Region Validation of model assumption Overall none of the assumptions in any of the models and databases were violated. To identify and catalogue individuals, only good quality photos of dolphins showing permanent marks were used so that marks could be recognised and identified on each sighting occasion. Marks including scars, pigmentation, or dorsal fin shape were used as secondary identification keys to allow a better distinction between similar individuals (Validation of Assumption 2). Photo-identification is a non-invasive technique, it avoids problems of dolphins being captured or retrieved from the environment, and hence heterogeneity in capture probabilities as result of behavioural response to the first capture occasion is unlikely (Validation of Assumption 3). Sampling occasions selected for analysis were relatively short in duration (6 months) in comparison with the dolphin s lifespan (decades) (Validation of Assumption 4). Results of TESTS 2 and 3 also indicate that the assumptions of equal capture and survival probabilities were not violated (Table 6.2). As only three sampling occasions were used to estimate the humpback dolphin population size in the NR, results for Tests 2 and 3 are not available. However, as only adult dolphins were used for population estimates analysis, and photo-identification is not an invasive technique, it is acceptable to assume that all individuals had a similar survival probability between capture occasions. In addition, due to the remoteness of the area and absence of any tourist activity, adult 127

150 dolphins were likely to show similar behavioural response to the presence of the research vessel. Furthermore, the complete coverage of the area surrounding the NR indicates that the core area used by the local population was entirely surveyed during each winter, therefore capture probabilities are likely to be similar. Furthermore, capture probabilities were always relatively high (>0.6), therefore, it was considered that Assumptions 1 and 5 were also validated. Table 6.2. Validation of the assumptions involved in Jolly-Seber capture recapture models used for the estimation of population sizes of humpback dolphins along the Capricorn Coast. Test df Chi-square P-value KB 2 groups TEST TEST KB Adults TEST PC 2 groups TEST TEST PC Adults TEST NR Adults TETS 2+3 NA NA NA Population estimates of humpback dolphins Marked and total population size Accurate parameter estimates were reached only for the model with constant probability of survivorship, capture and entry (p(c)φ(c)pent(c)). No other model showed goof fit of the data, as ΔAICc values for the remaining reduced models were always > 2, therefore results are presented only for the best model. Mark Recapture estimates indicate that each core area support a population of multiples of tens rather than hundreds of individuals (Table 6.3). Keppel Bay, with a total estimate of 107 dolphins (CV = 0.05) was used by the largest number of dolphins within the study period. In comparison, both Port Curtis (N = 85 CV = 0.05) and the Northern Region (N = 64 CV = 0.08) had smaller populations of humpback dolphins. Population size of mature and immature humpback dolphins 128

151 Accurate convergence of parameter estimates was reached only for the model with constant probability of survivorship, capture and entry (Table 6.3). None of the other models tested showed a good fit to the data. Results from that model indicate that about 2/3 of each subpopulation is formed by mature, and 1/3 by immature dolphins. Overall less than 70 mature and 35 immature dolphins live in each region (Table 6.3). Estimates of humpback dolphin population sizes per season In all three sites seasonal estimates of population sizes increased with the time, which suggests that none of the subpopulations was closed. However, the overall 95%CI are similar, which remained slightly below the total population size, suggesting that there was low seasonal variation and a population principally composed of resident individuals (Table 6.4). Analysis of seasonal estimates using the 2 groups model shows that the increment in population size is the result of an increment in the number of mature, rather than immature individuals, whose estimates remained more or less constant (Table 6.5). Approximate estimates of humpback dolphin population sizes in the Capricorn Coast Considering that in the regional population estimates some dolphins were counted twice, fewer than 250 humpback dolphins of each age class and less than 180 mature individuals populate the Capricorn Coast Region (Table 6.3). 129

152 Table 6.3 Abundance estimates of humpback dolphins in the Capricorn Coast between January 2006 and October In this table only the model that best fitted the data according to the AICc is shown. In the table Mt = model type (2Gr = 2 group model; 1Gr = 1 group model); np = number of estimable parameters in the model; D = dataset (A = adults; J = juveniles). Other notations: n = number of animals captured; Ň= estimates of marked animals; SE = standard error; CV = coefficient of variation; CI = confidence interval; θ = proportion of identifiable animals; N = estimate of total population size after correcting for proportion of identifiable individual Area Model Marked estimates Total population X Mt Description np AICc D n Ň SE 95%CI θ N T se CV 95%CI PC 2Gr Φ(c)P(c)Pt(c)N A J Gr Φ(c)P(c)Pt(c)N A KB 2Gr Φ(c)P(c)Pt(c)N A J Gr Φ(c)P(c)Pt(c) A NR 1Gr Φ(c)P(c)Pt(c)N 4 89 A J 42 na na 2.84 na na 0.71 na na 4.82 na 0.08 na na 1Gr Φ(c)P(c)Pt(c)N 4 89 A TOT M CC = M PC +M KB +M NR N CC = N T(PC) +N T(KB) +N T(NR)

153 Table 6.4. Seasonal abundance estimates of humpback dolphins in the Capricorn Coast between October 2006 and October 2008 using one group model. Letters and symbols are used as described in Table 6.3 and in the test. In the second column S = season either wet or summer (W) and dry or winter (D), the number indicating the capture occasion. 1 Group Marked estimates Total population Area S n Ň A(X) (t) SE 95%CI θ A(X) (t) S X (t) SE CV 95%CI PC W D W D KB W D W D NR D D D

154 Table 6.5 Seasonal abundance estimates of humpback dolphins in the Capricorn Coast between October 2006 and September 2008 using the 2 groups model. Letters and symbols are used as described in Tables 6.3, 6.4 and in the test. 2 Groups Marked estimates Total population Area S Gr. n Ň G(X) (t) SE 95%CI θ G(X) (t) S G(X) (t) SE CV 95%CI PC S1 A J W2 A J S3 A J W4 A J KB S1 A J W2 A J S3 A J W4 A J

155 6.4 Discussion An overall regional population estimate, obtained by summing estimates from each subpopulation, suggests that a total of about 256 humpback dolphins of all ages and about 177 mature humpback dolphins live along the Capricorn Coast. However, this is likely to be an overestimation of the real population size as some dolphins have been sighted in more than one region and thus counted twice. Nevertheless these estimates remain quite low, particularly given the large size of the study area. There are no substantial differences in the total estimates and in seasonal population size estimates within study sites, indicating that most dolphins are long-term residents. The increment in the population size per season is likely to be a consequence of movement of some individuals among core study areas. As no previous studies have been completed along the Capricorn Coast it is not possible to assess any trend in the population. The increment in the sub-population size between sampling occasions has various possible explanations. Analysis of the results from the 2 groups model suggests that the increment in population estimates between seasons is due to the variation in the number of adult individuals rather than juveniles. Analysis of photo-id data indicates that short-term movements of some adult individuals between study sites occurred. Therefore, this short-term movement rather than permanent immigration from regions outside the study area or a substantial increase in the resident population size, is likely to explain the increment in seasonal abundance estimates. For example, a large increase in the size of the Keppel Bay population was evident during the last sampling occasion. This growth is very likely to be related to the presence of trawlers in the region. For the first time during the study, in April-May 2008 a large increase in trawling activity was noted. Large schools of humpback dolphins, up to 45 individuals, were seen in association with 4 trawlers. During this same period a steep increase in the discovery curve is evident, with 16 new dolphins added to the catalogue. When trawlers subsequently left the area, 14 of these newly catalogued dolphins were not seen again, and a small school was noted following the trawlers away from the area. Trawlers provide an easy and constant food source, therefore it is possible that some of the humpback dolphins may have formed an association with trawlers, and they may have followed the trawlers from locations outside the study area. If all the sightings of humpback dolphins seen in association with trawlers are eliminated, the discovery curve shape more closely resembles that of a closed population, as shown from the genetic and distribution data. 133

156 The size of an animal population is a major determinant of its persistence in time. Small populations are more prone to extinction than large stable populations because of loss of genetic variability and environmental and demographic stochasticity (Caughley and Gunn 1996). Although there has been some controversy over how large populations must be to ensure persistence, recent studies across many vertebrate taxa indicate that the minimum size required for a population to be viable in the long term is thousands of individuals (Reed et al. 2003). More specifically, analysis of dolphin population viability indicates that dolphin populations with fewer than 100 individuals are extremely vulnerable to extinction (Lynch 1996, Thompson et al. 2000, Burkhart and Slooten 2003, Traill et al. 2007), while populations with 50 or fewer mature individuals are likely to become extinct within 50 years (Berger 1990). Considering this small population size within such a large area, the future of humpback dolphins from the Capricorn Coast must be considered insecure, unless effective conservation actions are taken. Although data on population trends are not available, the need for further protection of these dolphins is supported by evidence indicating that at the local level, anthropogenic related mortality may have contributed to the current low numbers. Two of the three sub-populations identified in this study live in areas (Keppel Bay and Port Curtis) that are highly impacted from fisheries, urbanisation, and industrialisation of the coastal areas in particular. High levels of anthropogenic contaminants and nutrients have been detected in the food chain, as well as in the sediments, with increased pollutant concentrations during floods (Brodie and Mitchell 1992, Jones et al. 2005). Trends in recreational fishing catches from 1977 to 2000 (Platten et al. 2008) showed a significant decline in catches per species across the entire period in the inshore waters, a trend not detected in the offshore waters. From analysis of photographs taken along the Capricorn Coast during the study, 29% of humpback dolphins show human related injuries such as net marks around the dorsal fin, or mutilation caused by entanglement and boat strikes (Author unpublished data). Between 2002 and 2009 in Port Curtis and Keppel Bay, 12 humpback dolphin carcasses were recovered along the shore. In 9 cases, the death was caused by human activity, while no marks or signs were present on 2 immature males and 1 adult female, for whom the cause of death was undetermined (Queensland Marine Wildlife Stranding and Mortality database reports ). No mortality data are available from the remote Northern Region where illegal fishing is still a common practice, and defence force activity including bombing occurs. Dolphins from this Northern Region area showed clear signs of distress in the presence of the research boat, by swimming away at high speed when approached 134

157 at a distance of about 100 m. Whether this behaviour is related to the intense navy activity in Shoalwater Bay during military exercises or other types of harassment is unclear. A study from South Africa suggests that an anthropogenic mortality rate of 7.5 humpback dolphins per year from a population of 200 individuals is not sustainable (Karczmarski 2000). Therefore, the documented mortality of almost 2 humpback dolphins per year between Port Curtis and Keppel Bay in a population of less than 180 individuals, may be close to or exceed the local population replacement rate. Furthermore, this has to be regarded as the minimum level of anthropogenic mortality in the region. At present, the protection of humpback dolphins in the Capricorn Coast area is inadequate. Port Curtis is not part of the GBRMP and the presence of a Dugong Protection Area B does not provide adequate protection to dolphins. Only part of Keppel Bay is included in the GBRMP, but most coastal waters where humpback dolphins occur are either a general use zone, or like most of the Fitzroy River and estuary, unprotected. Consequently, humpback dolphins inhabiting the Capricorn Coast are under high risk of local extirpation, unless effective management actions occur to reduce the level of human impacts on the population. 135

158 Chapter 7. Population estimates of Australian snubfin dolphins in the Capricorn Coast region In this chapter mark-recapture closed population models were applied to estimate the abundance of Australian snubfin dolphins in the Capricorn Coast region. Sightings of snubfin dolphins recorded during the study were used to obtain the population home range and to update the distribution records for snubfin dolphins in Australian waters. 136

159 7.1 Introduction The Australian snubfin dolphin (Orcaella heinsohni), was recently recognised as a separate species regionally endemic to Australia and possibly Papua New Guinea (Beasley et al. 2005). However, despite its importance as the only recognised regionally endemic cetacean species, basic information on the ecology of this species is lacking. Knowledge of the congeneric Irrawaddy dolphins cannot be directly applied to snubfin dolphins, as most studies on Irrawaddy dolphins were on river populations, whereas Australian snubfin dolphins are known to be a coastal marine species and therefore they have different ecological characteristics. Consequently, more studies are needed on snubfin dolphins to provide key information for management to guarantee the long term survival of this endemic cetacean species. Snubfin dolphins pose a particularly difficult challenge to researchers as a result of their coastal-estuarine distribution, naturally small school size and elusive behaviour, and the large sampling effort needed to collect an amount of data sufficient to conduct quantitative population studies. The first photo-identification study on snubfin dolphins in Australian waters was completed in 2003, in Cleveland Bay, North Queensland (Parra and Corkeron 2002, Parra et al. 2005). Until that study there was no detailed information available on the ecology and abundance of this species in Australian waters. Initial data on snubfin dolphins collected during aerial surveys designed to estimate the distribution and abundance of dugongs in the southern Great Barrier Reef (Marsh et al. 1996) were considered not suitable to estimate the abundance of snubfin dolphins due to the low number of sightings and problems of species identification (Parra et al. 2002). Similarly, an initial estimate of about 1000 snubfin dolphins for the western Gulf of Carpentaria calculated using data from aerial surveys (Freeland and Bayliss 1989) is highly questionable, due to the survey design and data collection protocol applied (Parra et al. 2002). Estimates of population size and distribution are important components of the information needed to manage cetaceans, and human impacts (Hooker et al. 1999; Wilson et al. 1999; Ingram and Rogan 2002; Hastie et al. 2003). Both distribution and population abundance estimates are fundamental to assessing the status of a species, and are key aspects of information required to list a species or a subpopulation under the IUCN Red List or the Australian EPBC Act

160 However, obtaining accurate and precise estimates of distribution and abundance of cetaceans is usually difficult, expensive and time consuming (Gerrodette 1987, Taylor and Gerrodette 1993), especially when baseline information such as occurrence and distribution are lacking. This is the case for the snubfin dolphins in coastal waters of Central Queensland (Capricorn Coast region). While some information on this species is available from the Northern Territory (Freeland and Bayliss 1989), Western Australia (Preen et al. 1995), and North Queensland (Heinsohn 1979, Parra et al. 2005, Parra 2006, Parra et al. 2006), in the Capricorn Coast region, no confirmed sightings of snubfin dolphins have been reported even where survey effort has been high and surveys were done in appropriate habitats (Preen 1999). Therefore, when this study was initiated there was no published information for this species along the Capricorn Coast apart from a few occasional stranding records. This chapter presents the results from the second photo-id study on snubfin dolphins completed in Australian waters (Parra et al. 2005). The aim of this research was to provide accurate estimates of population sizes and the range patterns of snubfin dolphins in the Capricorn Section of Great Barrier Reef Marine Park, Queensland, Australia. These results will provide important information to enhance the conservation management of this species in Australia. 7.2 Methods Survey procedures, data collection and effort Refer to Chapter 3 for details on survey procedures, including maps of the study area (Chapter 3 Figs. 3.3 and 3.4), survey effort, data collection and definitions Photo-identification, data collection and selection Snubfin dolphins were identified using multiple features, as marks on the dorsal fin were not always sufficient to distinguish among individuals. Deep body scars and wounds caused by sharks or other sources were used as secondary identification features. Photos taken during surveys were divided into three categories: 1) suitable for photoidentification matching, i.e. photos included the entire dorsal fin and dorsal fin base, 138

161 whose quality allowed the primary and secondary marks to be distinguished, 2) suitable for pod composition analysis, i.e. photos whose quality allowed each animal in the school to be distinguished, but were not suitable to distinguish individual dolphins between schools, and 3) not suitable for any analysis, i.e. poor quality photos. Only photos belonging to the first category were used to catalogue new individuals and to distinguish among them. As it was often not possible to take photographs of both left and right sides of the dorsal fin, a separate catalogue was kept for each side of the dorsal fin. A subsequent comparison was done to create the final catalogue that included all individuals unequivocally identified by both sides of the dorsal fin and those identified by only one side. To obtain an adequate sample size and to guarantee that all the dolphins were available to be photographed during any capture occasion, data were pooled into seasons corresponding approximately to dry (winter) and wet (summer) seasons, for a total of four capture occasions: (1) October 2006 to April 2007, (2) May to September 2007 (3) October 2007 to April 2008, and (4) May to October This resulted in capture history files composed of four capture occasions coinciding with the four sampling seasons. Due to the initial limited knowledge of the study area and the slow identification rate, surveys conducted in 2006 were excluded from the population analysis to minimize variation in capture probabilities between sampling occasions Validation of mark recapture assumptions The application of mark recapture methods requires the validation of five basic assumptions, and failure to validate these assumptions will result in a serious bias in population estimates. The five assumptions have been described in Chapter 6 in the section The five assumptions were analysed and validated as specified below. Assumption 1: within a population, all individuals have the same probability of being captured during any occasion. Violation of this assumption will result in an underestimation of population size. During the study, attempts were made to photograph every individual in a school, while preferentially photographing any particular individual was avoided. Goodness of Fit Test (GOF) for heterogeneity of trapping probabilities, indicates that accounting for heterogeneity as source of variation did not improve the model fit (M O vs M h : χ 2 = 2.09, df = 2, P =0.35). 139

162 Assumption 2: Mark-recapture analysis assumes that each dolphin in the catalogue will be unequivocally recognised and that marks are not lost for the entire period of the study. Failure to do so will bias estimates of population size upward. In this study, individuals were identified only using long term marks distinguished during the screening of good quality photographs. Furthermore, only adult individuals were catalogued, diminishing the probability of mark loss or change. Under assumption 3, all marked animals must have the same probability of being recaptured after the first capture occasion, i.e. there is not an altered behavioural response that will alter the probability of capture as result of the survey technique. Failure to do so will result in an overestimation of population size in the case of "trap shy" behaviour, or underestimation of population size in the case of "trap happy" behaviour. As photo-identification uses existing marks it involves no physical interaction between animals and researchers, and therefore behavioural responses are unlikely. Furthermore, the test for behavioural response after initial capture (M o vs M b : χ 2 = 0.158, df = 1, P =0.69) indicates that behavioural response was negligible in this study. Assumption 4: sampling must be instantaneous. Failure to do so will result in positive bias estimates. Sampling occasions selected for analysis were relatively short (5 months) in comparison with the dolphin s lifespan (decades). These mark recapture assumptions are considered validated, therefore bias in population estimates is considered to be unlikely or minimal. Finally, assumption 5 deals with the problem of permanent emigration, however as the Keppel Bay snubfin population was classified as closed, permanent emigration is not accounted for the time of the study Closed versus open population models While open population models allow for immigration and emigration into the population, closed population models assume that all individuals are available to be captured in the study area at any time (geographic closure) and that there are not unknown changes in the abundance during the study (demographic closure), as closed population models can account for known changes (White et al. 1982). The likelihood of population closure was evaluated using GIS based methods to test for geographic closure (i.e. if the entire area 140

163 used by snubfin dolphins was surveyed), and a combination of graphics and statistical methods to test for demographic closure (immigration and emigration or birth and death). To determine geographic closure the overall distribution of snubfin dolphins was represented using the 95% UD. The Utilization Distribution (UD) is a probability density function that describes the relative use of space by an animal within a defined area based on a sample of animal locations (Van Winkle 1975). The UD for snubfin dolphins was estimated at the population level rather than the individual level, using locations of schools of animals rather than of individuals (after Parra 2006). The ArcGis Animal Movement Analyst extension was used to estimate a fixed kernel UD (Hooge & Eichenlaub 1997). A kernel range of 95% was selected as it is considered to be the most robust estimator of animals home range (Worton 1989), and was estimated using smoothing parameters calculated via the least squares cross-validation procedure (Seaman et al. 1999). To determine demographic closure, a discovery curve of the cumulative number of identified individuals, against the survey effort in hours, was plotted to assess if the majority of dolphins were identified by the end of the study. However, while this method allows for detection of immigration into the population over the study period, it does do not allow detection of either temporary and permanent emigration or temporary immigration. To test for closure to emigration and immigration, the survey data were further analysed using two closure tests, Otis et al. (1978) and Stanley and Burnham (1999), both available in the CloseTest program (Stanley and Burnham 1999). The test provided by Otis et al. (1978) was developed under a null model allowing for heterogeneity in capture probabilities. This test is sensitive to the presence of time or behavioural variation in capture probabilities, but insensitive to temporary violation of closure occurring during the middle of the study (Otis et al. 1978, White et al. 1982). The Stanley and Burnham (1999) test is the more robust of the two, and was developed under a null model allowing for time-specific variation in capture probabilities. It tests the null hypothesis of a closed population time model, against the alternative hypothesis represented by the open population Jolly-Seber model. This test is sensitive to permanent and to temporary emigration and immigration. The Stanley and Burnham (1999) test can be further broken down into four component statistics, testing if closure was violated due to significant immigration (NR vs JS and Mt vs NM) or emigration (NM vs JS and Mt vs NR) (refer to Stanley and Burnham 1999 for more details). The use of both tests allows a better detection of violation of closure assumptions (Stanley and Burnham 1999). 141

164 7.2.5 Population estimates The population sizes of snubfin dolphins were estimated using closed population models. Estimates using closed population models were obtained from 8 different models derived from the combination of three sources of variation in encounter probabilities: time (t), behaviour (b) and individual heterogeneity (h). The 8 closed population models are M(o), M(t), M(h), M(b), M(th), M(tb), M(bh), and M(thb) (Otis et al. 1978). M(o) is the null model with constant detection probabilities across all three factors. Each model was built and run in the program MARK following the procedure described in White (2008), and using the Full Closed Capture with Heterogeneity parameterization. Models were listed using the quasi-aicc value corrected for small sample size (QAICc) to account for minor data overdispersion as indicated by c.hat (ĉ) values ranging from 1 to 3: QAICc = Dev ĉ 2k(k + 1) + 2k + (Ň K 1) Where k is the number of parameters for a specific model, Dev is the model deviance and Ň is the population estimate for marked individuals. However, as ĉ values increase, it is more likely that the best fit model will be the simplest model with fewer parameters resulting in population estimates that are too optimistic and with small variance. Therefore, in a situation where a range of models provides good fit to the data, more complex models allowing for unequal catchability are preferred (Amstrup et al. 2005). As more than one model often provided a good fit to the data, to account for model selection uncertainty, parameter estimates were model-averaged among all models with ΔQAICc values less than 2 from the best model, excluding the null model (M o ) (after Burnham and Anderson 2002) Total population size and population size of mature individuals Total population size N was estimated as: N = Ň θ where Ň is the mark-recapture estimate of the number of dolphins with long-lasting marks, while θ is the proportion of dolphins with long-lasting marks in the population, calculated among all individuals photographed with first category photos. Standard error and 95%CI were derived from the variance of N: 142

165 Var N = N 2 VarŇ Ň θ nθ where n is the total number of animals from which θ was estimated (Williams et al. 1993, Chilvers and Corkeron 2003). Population size per sampling period was estimated from the best of the 4 models including the time (t) as source of variation. The proportion of marked individuals was estimated among all individuals photographed per sampling period with first category photos. Total population size of mature individuals (N ma ) was estimated following the same procedures used to estimate the total population size. To calculate the proportion of marked mature dolphins, it was assumed that all individuals of about 2 m in length, identified as adults were also mature and all individuals less than 2 m long and classified as juveniles or calves were immature (from Parra et al. 2005). The marked population size (Ň) was corrected with the proportion of marked mature individuals (θ ma ). The proportion of marked mature dolphins was estimated from the total number of adult individuals observed, excluding juveniles and calves Population estimates using POPAN open population model A closed population is a particular case of a population with no immigration or emigration. However open population models do not account for variation in capture probabilities, which closed population models do. In this chapter, results from open population models are also presented to allow comparison with other studies and to obtain population estimates for each sampling period. Refer to the methods section in Chapter 6 for details on the open population model procedures. 7.3 Results Photo-identification data Between 2006 and 2008 a total of 179 schools of snubfin dolphins were sighted in the study area, however good quality pictures suitable for identification were obtained only 143

166 Jan-06 Feb-06 Mar-06 Apr-06 May-06 Jul-06 Aug-06 Sep-06 Oct-06 Nov-06 Jan-07 Feb-07 Mar-07 Apr-07 May-07 Jun-07 Jul-07 Aug-07 Sep-07 Oct-07 Nov-07 Jan-08 Feb-08 Mar-08 Apr-08 May-08 Jun-08 Jul-08 Aug-08 Sep-08 Oct-08 Survey efforts in hours Cumulative number of dolphins identified from 115 schools, resulting in a total of 334 photos of good quality. From these photos 54 adult snubfin dolphins were identified. Only three juvenile snubfin dolphins had long-term distinguishing marks, but these were not included in the analysis. By the end of 2006 about 62% of marked individuals were catalogued, and the proportion of identified dolphins reached 70% by the end of 2007 (Fig 7.1). A large group including 13 new marked individuals was added to the catalogue in the third sampling period. Snubfin dolphins showed a medium to high site fidelity to the Keppel Bay study area with 38 dolphins (70%) sighted every sampling period, and 44 dolphins (81%) identified in at least two different seasons (Fig. 7.2). Most of the dolphins were sighted on more than one occasion, with only four dolphins identified once. The overall number of sightings per identified dolphin was influenced by the large extent of the study area, which was impossible to survey in one day, and by the often adverse weather conditions that limited the survey effort Figure 7.1 Discovery curve showing the cumulative number of snubfin dolphins (grey line) identified in relation to hours of survey per month (histograms) in Keppel Bay between 2006 to

167 Dolphins identification numbers Figure 7.2. Sighting frequency for the 54 snubfin dolphins identified in the Keppel Bay core areas Model selection During the study the entire area used by snubfin dolphins was comprehensively surveyed, including surrounding regions to the north and south of Keppel Bay. The representative range (95% kernel range) of snubfin dolphins was split into two parts covering a total area of about 2,270 km 2 (Fig. 7.3). The presence of a separate area in the proximity of Coorio Bay does not indicate a second population; as sightings of these snubfin dolphins in the area were scarce, obtained within a very short time frame of a month, and the individuals identified in the proximity of Coorio Bay were also observed within the main home range boundary. No good quality photographs were obtained for the single snubfin dolphin school sighting in Shoalwater Bay, but, considering the absence of records along Nine Mile Beach and the Northern Region, that was likely to be a vagrant school and therefore the model did not include that record within the home range boundary. 145

168 The 95% distribution range and field data suggest that the Keppel Bay population of snubfin dolphins is geographically isolated. No known resident population is found south of the Fitzroy River (D. Cagnazzi data from unpublished surveys), and the nearest known aggregation is found in Repulse Bay, North Queensland, about 500 km north (Parra and Cagnazzi unpublished data). Therefore, a substantial immigration or emigration to and from neighbouring populations is considered unlikely during the time of the study. In contrast, the shape of the discovery curve (Fig. 7.1), with a plateau in late 2007 followed by a steep increase in the curve in early 2008, suggests that the population is open to immigration of schools rather than a constant immigration of a few individuals from surrounding regions. The large number of individuals catalogued during the third capture occasion coincides with sightings of a large social school, where the dolphins were not alarmed by the presence of the research vessel. This situation is rare but ideal for photo-identification purposes as it provides the opportunity to obtain good quality photos in a short time of several individuals that may have been missed previously. The steep increase in the proportion of marked individuals during the same period suggests that those dolphins were never catalogued before because they were unmarked (see Table 7.3). Alternative hypotheses to explain the observed pattern of immigration are: 1) short term movements of some individuals outside of their normal range that would have made them temporarily unavailable to capture, or 2) dolphins were available to capture but were not previously photographed, or 3) photos were not suitable for identification purposes. Results from the Otis et al. (1978) and Stanley and Burnham (1999) closures tests indicate that the population was closed (Otis et al. (1978): Z = P = 0.26; Stanley and Burnham χ 2 = 5.14, df = 4, p = 0.27). The low p values from the Stanley and Burnham tests for additions to the population indicate that there was a non-significant addition of dolphins to this population (NR vs. JS: χ 2 = 4.93, df = 2, p = 0.08; Mt vs. NM: χ 2 = 4.92, df = 3, p = 0.17), while high p values of the tests for emigration or losses indicate that there were no deletions from the population during the study (Mt vs. NR: χ 2 = 0.21, df = 2, p = 0.90; NM vs. JS: χ 2 = 0.22, df = 1, p = 0.63). Based on these results, the Keppel Bay snubfin dolphin population can be considered closed for the period of the study, and therefore the application of closed population models is appropriate. 146

169 Figure 7.3 The 95% distribution range of snubfin dolphins is concentrated in the Fitzroy River area. A few schools were also observed in proximity of Coorio Bay, and only one school was observed north of Coorio Bay. A single dolphin was also sighted in proximity of Cape Capricorn but this was likely to represent an individual outside of the normal range. 147

170 7.3.3 Population estimates Population estimates using closed population models In reference to the QAICc values, the models were ranked from the simplest model to the model including all three sources of variations, m, t, h (Tab. 6.1). Excluding M o, three models M b, M h and M tb were within ΔQAICc of 2 indicating a good fit to the data. Estimates of population size of dolphins with long-lasting marks from model averaging across M b, M h and M tb was Ň= 55 (Ň = 55.52, SE = 2.58, 95%CI = ). The total population estimate considering the proportion of marked individuals (θ = 0.75), was N = 74 (N = 74.03, SE = 4.14, 95%CI = ). Marked and total population estimates for each model are provided in Table 7.1 and 7.2 respectively. The accuracy of seasonal population estimates and their 95%CI using the closed population model were affected by the high standard error on the capture probabilities, and results are shown in Table 7.3. The estimated population size of mature individuals (N ma ), estimated adjusting the marked population size with the proportion of adult marked individuals was 66 (θ ma = 0.84, N ma = 66.1, SE = 3.44, 95%CI = ). Table 7.1. Population estimates of marked snubfin individuals (Ň) for the 8 closed population models. QAICc = Quasi-Akaike Information Criterion value corrected for small numbers, NP = number of parameters, SE = standard error, 95% CI = 95% confidence interval, CV = coefficient of variation. Model QAICc ΔQAICc NP Ň SE CV 95%CI M o / M b M h M tb M bh M th M tbh M t

171 Table 7.2. Total population size (N) of snubfin dolphins for the 8 closed population models. The proportion of marked individuals was θ = In the table SE = standard error, 95% CI = 95% confidence interval, CV = coefficient of variation. Model N SE CV 95%CI M o M b M h M tb M bh M th M tbh M t Table 7.3. Marked (Ň) and total population size (N t ) for each capture occasion of the Keppel Bay snubfin dolphin population, estimated using M tb close populations model. In the table SE = standard error, 95% CI = 95% confidence interval, CV = coefficient of variation, and θ = proportion of marked individuals. Model n p SE Ň SE CV θ N t se 95%CI Population estimates using open population models Assumptions 2 and 4 outlined in paragraph are considered validated. The pool χ 2 statistic for GOF Tests 2 and 3 (Test2+Test3), indicates that the assumptions of equal capture and survival probabilities (1) and permanent emigration (5) were not violated (χ 2 = 5.37, df = 4, P = 0.25). Pradel s test for trap-dependence showed no indication of traphappy or trap-shy behaviour by marked individuals (Assumption 3) (Z = 0.52; df = 4; p = 0.53). Of the 7 basic models tested, numerical convergence of the parameter estimates was reached in six occasions, but accurate estimates of each parameter were obtained only for one model Phi(c), p(t) PENT(c)N. (i.e. capture rate varies with time while survival and 149

172 probability of entry are constant). As a result, no model had a ΔQAIC < 2 from the best model (Burnham and Anderson 1998). Two models had a ΔQAIC < 2 indicating a good fit to the data. Total population size derived from the best model accounting for the proportion of marked individuals was 73 (θ = 0.75, N = 72.83, 95% CI = ; CV = 0.04) (Table 7.4). Seasonal population estimates for the selected model are similar to the overall population size (Table 7.5). Table 7.4. Overall marked (Ň) and total population estimates (N) of snubfin dolphins in Keppel Bay using POPAN parameterisation for open populations. The proportion of marked individuals θ was In the table SE = standard error, 95% CI = 95% confidence interval. Selection criteria Marked population Total population Model QAICc ΔQAI Cc np N SE 95%CI N SE 95%CI Φ c p t b c Φ t p c b c Φ c p t b t Φ t p t b t Φ c P c b c 33, Φ t p c b t 33, Table 7.5. Marked (Ň) and total population size (N) for each of the four capture occasions t of the Keppel Bay snubfin dolphin population, estimated using POPAN open populations model. In the table SE = standard error, 95% CI = 95% confidence interval, CV = coefficient of variation, and θ(t) = proportion of marked individuals for a particular capture occasion. t Ň(t) SE Lower Upper θ(t) N(t) SE 95%CI

173 7.4 Discussion Geographic isolation During this study, sightings of snubfin dolphins were concentrated in Keppel Bay with only two records obtained in different regions, one in Shoalwater Bay and one near Cape Capricorn along the eastern side of Curtis Island (Fig. 7.2). In Shoalwater Bay the attempted photo-identification was unsuccessful, while the record near Curtis Island was of a well known animal normally resident in Keppel Bay. In 2008, no snubfin dolphin was sighted in a series of line transect surveys conducted over 14 days between May and September from Cape Palmerstone (21.53 S, E) to Mackay (21.12 S, E) as part of a study aiming to develop spatial models of inshore dolphin distribution along the Queensland coast (Guido Parra and Cagnazzi Daniele unpublished data). However, during boat based surveys done for the same project between Mackay and the Whitsunday Islands region (20.07 S, E) in September 2008, sightings of snubfin dolphins became consistent and numerous only from Repulse Bay (20.63 S, E) at the southern end of the Whitsundays (Guido Parra and Cagnazzi Daniele unpublished data). This indicates that the nearest snubfin dolphin population to the Keppel Bay population occurs about 500 km to the north. In the Queensland marine stranding and mortality database (Environmental Protection Agency), there are only four records of snubfin dolphins between Keppel Bay and Repulse Bay, three of which occur in proximity to Mackay (JM Sept-1974, W Aug- 1992, W22 05-Aug 1996) (Parra et al. 2002), and one at Stockyard Point (W Aug- 2002) only 50 km north of Keppel Bay. Together, these data indicate that the Keppel Bay population of Australian snubfin dolphins is relatively geographically isolated. This hypothesis is supported by the genetic evidence presented in Chapter Population size Estimates of population size using both open and closed population models were similar, indicating that fewer than 80 snubfin dolphins live in the Keppel Bay region. Overall estimates of capture probabilities (p) were low. Low capture probabilities were probably the result of the large extent of the study area and of the few hours of survey completed in good sea state conditions, as even in low wind conditions, tide affected the visibility. The 151

174 estimates of absolute abundance obtained from this study are the first available for this population. The size of the Keppel Bay population is similar to that obtained by Parra in Cleveland Bay, North Queensland (Parra et al. 2005), but population estimates from Keppel Bay are from a substantially larger area. In comparison, estimates for sympatric humpback and Irrawaddy dolphins in South East Asia and the Philippines are slightly higher than that recorded in this study (Smith and Hobbs 2002, Kreb 2002, Smith et al. 2006, Stensland et al. 2006). The only exceptions are for the Pearl River, and Sundarbans where humpback and Irrawaddy dolphins are found in high numbers (Jefferson and Hung 2004, Smith et al. 2006). In contrast, estimates for isolated coastal bottlenose dolphin populations elsewhere in the world are generally higher and consist of more than 100 individuals and up to thousands of individuals (Wells and Scott 1990, Williams et al. 1993, Wilson et al. 1999, Stensland et al. 2006, Reisinger and Karczmarski 2009). The small population size in Keppel Bay, coupled with the geographic and likely genetic isolation highlights concerns about the vulnerability and long term viability of the snubfin dolphin population in Keppel Bay. More protection is required for this population, because the area where snubfin dolphins are most often sighted is in the Fitzroy River and estuary, which are not included in the Great Barrier Reef World Heritage Area. This situation is of even more concern when the absence of snubfin dolphin sightings along other parts of the Capricorn Coast, is taken into consideration. Thus the total population estimates for the entire Central Queensland coast are likely to remain in the order of about 100 individuals, increasing concerns about the long term survival of snubfin dolphins in this region. The loss of the Keppel Bay population as a result of habitat degradation would substantially reduce the distribution range of snubfin dolphins in Australia Distribution The recorded occurrence of Australian snubfin dolphins extends as far as the Brisbane River (27 32 S, E). However, evidence provided from this and previous studies indicate that there are no resident populations of snubfin dolphins south of Keppel Bay. During the long term boat based study completed in Port Curtis (Chapter 3), Hervey Bay and the Great Sandy Strait (Cagnazzi et al. 2011), no school of snubfin dolphins was sighted. Local tour operators and fishermen that were interviewed were also not aware of the presence of this species in these areas. Only one confirmed sighting was reported from Moreton Bay, southern Queensland (Paterson et al. 1998), despite the detailed 152

175 surveys completed during previous years on bottlenose and humpback dolphins (Corkeron et al. 1987, Corkeron 1990, Chilvers et al. 2003). In the Queensland marine and wildlife stranding and mortality databases (EPA) only two snubfin dolphin records are listed south of Keppel Bay. In 1994, a carcass was found in the proximity of the Burnett River (JM10574: 18 July 1994). However, surveys conducted by the author in that area between 2004 to 2006 resulted in no sightings of snubfin dolphins (Cagnazzi unpublished data). The second snubfin dolphin was found dead in shark nets set along Noosa Beach in 2007 (W1915: 6 June 2007). Considering the paucity of records the occurrence snubfin dolphins in Southern Queensland can be considered extralimital and it is likely that those individuals were vagrants. The term vagrant identifies individuals dispersing outside their normal range. The concept of vagrant dispersal was introduced by Lidicker (1975). He defined this dispersal as "...any movement of individual organisms in which they leave their home area, sometimes establishing a new home area. This does not include short -term exploratory movements, or changes in the boundaries of a home range, such that the new range includes at least part of the former. Dispersal thus produces homeless travelers (vagrants) who are in search of a new home." Dispersal is a three-stage process including emigration (crossing habitat boundaries), transience (movements trough non habitats) and settlement (immigration or colonisation) (Ims and Yoccoz 1997, Clobert et al. 2004). During the first two stages animals are expected to ignore several stimuli, including feeding needs, conspecific and biotope boundaries, to which they would strongly respond during routine movements (Van Dyck and Baguette 2005). Such behaviour is particularly true in fragmented landscapes where resources are found in discrete patches, often at a considerable distance relative to the scale of space-use by the average individual of a particular species (Van Dyck and Baguette 2005). Hence, during the first two stages dispersal is associated with high mortality risks as a consequence of predation, starvation and limited knowledge of the area and anthropogenic hazards. The relatively high proportion of snubfin dolphins found dead in southern Queensland compared to live sightings may be a consequence of the higher mortality risk associated with emigration and transient movement events. Mortality of dispersing individuals during migration negatively influences the likelihood of colonization success (Gustafson and Gardner 1996). Other factors affecting colonization 153

176 success are interspecific competition, habitat characteristics, and quantity and quality of food resources. Climate is fairly similar along the Central Queensland coast, therefore it is unlikely to be limiting snubfin dolphin distribution. One difference between regions is that, in Southern Queensland the coastal habitats are occupied by two inshore dolphin species, humpback dolphins (Sousa chinensis) and inshore bottlenose dolphin (Tursiops aduncus), whereas in Central and Northern Queensland snubfin dolphins share the coastal habitat only with humpback dolphins. Sightings of bottlenose dolphins along the Central and Northern Queensland coast are rare as these dolphins tend to be found further offshore (author unpublished data, and Guido Parra personal communication). If coastal estuarine habitats are not able to support three species of resident dolphin populations in these northern regions, interspecific competition for food resources and habitat needs, could result in the exclusion of the less competitive species. Snubfin dolphins are smaller and slower compared to humpback and bottlenose dolphins and hence they may be less able to compete for food and habitat resources against the two larger dolphin species. In view of the information presented in this study, a review of the distribution of snubfin dolphins in Queensland is necessary. The southern boundary of Australian snubfin dolphins (Orcaella heinsohni) should extend as far south and include the entire Fitzroy River estuary until Cape Capricorn and the beginning of the Narrows (Fig 5.2). 154

177 Chapter 8. General Discussion 155

178 8.1 Introduction As noted in the Introduction chapter, humpback and snubfin dolphins are among the least known cetacean species in Australian waters. Prior to 2002, very little was known about the abundance, distribution and status of both species, despite the range of both species encompassing a large proportion of Australian tropical coastal waters. Even today information remains scarce, with studies completed only in three coastal areas in Central and Southern Queensland: Moreton Bay, the Great Sandy Strait and Cleveland Bay (Corkeron 1990, Parra et al. 2002, Cagnazzi et al. 2011). Therefore this study has greatly increased the available information on humpback and snubfin dolphins in Queensland coastal waters, but only for a small fraction of the distribution range of both species. The peculiarity of this study is that it covered a very large area. Therefore in the relatively short period of time of this study, fundamental baseline information such as distribution and abundance has been obtained for both humpback and snubfin dolphins for a large area. Furthermore, this study allowed a collection of new information on movement, connectivity and gene flow among geographically separated subpopulations of humpback dolphins, aspects that had not previously been studied as most studies are spatially limited. Spreading the survey effort over a large area limits the amount of data collected within areas and per individual, limiting analysis on social structure, home range and behaviour which require large amounts of re-sightings per individual. In this final chapter the information obtained in this study has been summarised and compared to that from studies on inshore dolphins in other regions. Secondly, this information has been used to assess the conservation status of humpback and snubfin dolphins in the Capricorn Coast, using the criteria provided by the IUCN Red List for regional populations. Finally, some directions for future research have been outlined. 8.2 Summary of major results from this study Survey limitations and related issues From the beginning of this study it was clear that the fieldwork organisation would be a major challenge. Because of the absence of information on dolphin distribution and 156

179 occurrence in most of the Capricorn Coast, during the first sampling period survey effort was focused on covering the entire region regardless of the likelihood of finding dolphins (Chapter 3). This has provided more accurate knowledge and unbiased information on the distribution of dolphins in the Capricorn Coast region. This information was used to design a better survey plan that was applied in the following two sampling periods. The large amount of effort spent in areas not commonly used by dolphins has consequently limited the amount of information collected during the first sampling period. Therefore, these data were not used in the population estimates. The combined effects of strong south-easterly winds and late morning seabreezes affecting the Capricorn Coast region between October and April limited the survey periods available during the summer, which resulted in a substantial difference in the amount of data collected between summer and winter seasons. However, as only data collected in Beaufort sea state <2 were included in the analysis, the bias due to heterogeneities in sighting probability as a result of sea state is expected to be negligible. The limited number of accessible boat ramps and the long distances needed to be covered on a daily basis to reach the study areas also reduced the time available for collecting data on these dolphins Humpback dolphins: summary of results. The results from this study indicate that the distribution of humpback dolphins in the Capricorn Coast region is geographically clustered, with clear preferences for sheltered habitats in proximity or within inlets, bays and rivers rather than habitats consisting of long exposed coastline and open bays. This resulted in the identification of three major core areas along the Capricorn Coast: 1) Port Curtis, 2) Keppel Bay, and 3) the Northern Region (Chapter 3). Even though very few sightings of humpback dolphins occurred along the transition areas, analysis of photo-identification data indicates that some dolphins do move among core regions, and therefore travel through the transition areas (Objective 1). Analysis of social structure (Objective 2) provided similar results. Humpback dolphins identified in the Capricorn Coast were subdivided into three communities associated geographically to the three core study areas. Analysis of home range at the community level showed that each community has a well defined preferred range that does not overlap with those of the other communities (Chapter 4). Nevertheless a small level of association was recorded among individuals from different communities. Within core areas, resighting patterns vary among dolphins, with some individuals displaying an 157

180 apparent long term residence pattern to a particular core study area, while others appear to range throughout the entire region. Analysis of molecular variance (Objective 3) provided similar results to those obtained from the association pattern analysis. High gene flow was recorded among Keppel Bay and Port Curtis communities. No samples from the Northern Region have been collected, however it is likely that some gene flow occurs also between the Northern Region and Keppel Bay or Port Curtis, as some dolphins were sighted moving between this region and Keppel Bay, and one dolphin was sighted in all three regions. Therefore, it was concluded these three distinct social units form a large meta-population, connected by limited interaction, resulting from movements of some individuals among these areas. These results are similar to the patterns recorded for Sousa populations in Cleveland Bay (Parra et al. 2005), and in Maputo Bay, Mozambique, where most Indo-Pacific humpback dolphins are residents, but transient individuals join resident groups temporarily (Guissamulo 2000). In Algoa Bay, South Africa, humpback dolphins display varying degrees of site fidelity, with some members of the population being more-or-less resident, but most others range widely within a narrow band along the coast (Karczmarski 1999). Movement by individual dolphins of up to 120 km, similar to the movements recorded in this study, were observed along both the KwaZulu-Natal (Durham 1994) and Eastern Cape coast, South Africa (Karczmarski 2000), but this is the first time that movements of such distance have been recorded in Australia. Within core areas humpback and snubfin dolphins were sighted throughout the study with no major differences in sighting rates, school size and composition between regions, or within regions among seasons and years. These finding are similar to results from Cleveland Bay in North Queensland (Parra et al. 2004), where humpback dolphins were sighted year round with no apparent seasonal variation. This contrasts with patterns recorded for humpback dolphins in Algoa Bay where seasonal variation in occurrence, abundance, and group size is considerable (Karczmarski et al. 1999a), which results from seasonal immigration of humpback dolphins into and emigration from, the Algoa Bay region in summer (Karczmarski et al. 1999b). Similar to population estimates obtained from Cleveland Bay (Parra et al. 2004), and the Great Sandy Strait (Cagnazzi et al. 2011), humpback dolphins along the Capricorn Coast live in small communities with the entire population being composed of about

181 individuals and less than 200 mature individuals over an area extending more than 500 km (Objective 4). Less gene flow was recorded between dolphins from the Capricorn Coast and those from the Great Sandy Strait, with less than two individuals estimated to migrate per generation between these two regions (Chapter 5). This value is close to that of one individual per generation identified in the IUCN Red List as criterion used to classify a population as genetically isolated. At present, biopsy sampling is underway in the Whitsundays to obtain genetic information from humpback dolphin populations located in the north of the study area. These data will clarify if the limited numbers of sightings obtained during exploratory surveys conducted from Shoalwater Bay to Mackay (Chapter 3, Parra and Cagnazzi unpublished data), translates into an absence of gene flow among these regions. This information will be important for determining the genetic status of the Capricorn Coast humpback dolphins and consequently to apply the most appropriate conservation plans Australian snubfin dolphins: summary of results Snubfin dolphins were sighted only in the Keppel Bay core study area. Results from snubfin dolphin surveys were similar to those observed for snubfin dolphins in Cleveland Bay, with no apparent variation among seasons and years (Parra et al. 2005). However, compared to what was observed in Cleveland Bay, the Keppel Bay snubfin dolphin population appears to be closed with no apparent emigration or immigration to and from neighbouring populations (Objective 1 and 2). The geographic isolation of the Keppel Bay snubfin dolphin populations was supported by the absence of sightings of this species in Port Curtis and the Northern Region core study areas (Chapter 3 and 7), and from the absence of sightings of these species during exploratory surveys from Shoalwater Bay to Mackay (Chapter 3, G. Parra and D. Cagnazzi unpublished data). This hypothesis is also supported by preliminary results from the analysis of molecular variance (Objective 3; Chapter 5), with high levels of genetic differentiation between samples from Keppel Bay and North Queensland. However, these are preliminary results and the results need to be viewed with caution as the sample size was small and a very low level of polymorphism was recorded in the 27 microsatellite loci screened. More detailed results will be available once the ongoing sampling of other areas along the Queensland coast has been be completed. 159

182 Population estimates obtained in this study corroborate the results from Cleveland Bay (Parra et al. 2004). The Keppel Bay Snubfin dolphins population is small and composed of about 75 individuals with 95% CI always below 85 individuals (Objective 4; Chapter 7). The Keppel Bay snubfin dolphin population is of great significance, as it represents the southernmost population of this regionally endemic Australian species. Therefore, considering its small population size, geographic isolation and limited protection, where most of its home range is located in a non-protected highly industrialised area, a precautionary approach should be taken to guarantee the long term survival of this population. 8.3 Conservation status of humpback and snubfin dolphins in the Capricorn Coast Overview of IUCN list criteria and categories for the assessment of regional subpopulations Assessing the conservation status of cetacean species is difficult and costly, unless they have a very limited distribution. Nevertheless, even when survey effort is large, data remain insufficient to meet the assessment criteria required by conservation agencies at a national level. As a result, cetaceans are frequently overlooked when establishing priorities for biodiversity conservation (Currey et al. 2009). To overcome this problem the IUCN has provided a framework for assessing the threatened status of sub-populations (IUCN 2001), defined as geographically or otherwise distinct groups in the population, between which there is little demographic or genetic exchange (typically one successful migrant individual or gamete per year or less). A threatened sub-population may be classified as Critically Endangered (CR), Endangered (EN), Vulnerable (VU) or Near Threatened (NT). A taxon is Critically Endangered when it is considered to be facing an extremely high risk of extinction in the wild. A taxon is Endangered when it is considered to be facing a very high risk of extinction in the wild, and Vulnerable when it is considered to be facing a high risk of extinction in the wild. A taxon is Near Threatened when it has been evaluated against the criteria but does not qualify for Critically Endangered, Endangered or Vulnerable now, but is close to qualifying for or is likely to qualify for a threatened category in the near future 160

183 (IUCN 2001). Each of these criteria, depending on the conservation status has a series of sub-criteria to be met (see criteria on IUCN 2001). Here, the IUCN Red List criteria were applied to assess the conservation status of humpback and snubfin dolphin sub-populations in the Capricorn Coast region. A threatened sub-population may be classified in any of the threatened catagories previously described. If possible, sub-populations should be assessed against all criteria to ensure the highest possible threat classification is obtained (IUCN 2001). The assessments at a regional level follow two simple steps. In step one; the IUCN Red List Criteria are applied to the regional population of the taxon, resulting in a preliminary categorization. In step two, the existence and status of any conspecific populations outside the region that may affect the risk of extinction within the region should be investigated. If the taxon is endemic to the region or the regional population is isolated, the Red List Category defined by the criteria should be adopted unaltered. Alternatively, if conspecific populations outside the region are judged to affect the regional extinction risk, the regional Red List Category should be downgraded to the next lower conservation status (IUCN 2003) because if immigration from surrounding populations occurs, it is likely to have positive effects on the survival of a population. Globally, many regional cetacean populations are threatened due to human impacts that include hunting, incidental catch, pollution and prey depletion (Wells & Scott 1999; Reeves et al. 2003, Harrison et al. 2009). Coastal populations are especially vulnerable because they often have restricted geographic ranges, fragmented distributions, and limited movements (Reeves et al. 2003). Decline or extirpation of regional populations of coastal dolphins could have significant ecological effects on community structure and species endemic to that region, given their role as apex predators (Estes et al. 1998). Given that relatively small changes in predator ecology can have significant impacts on ecosystems, assessing and managing the threats to regional populations of coastal dolphins should be viewed as an important component of global biodiversity conservation. In the Indo-Pacific region, humpback and Irrawaddy dolphins are among the most threatened dolphin species. In the IUCN Red List the Australian snubfin dolphin (Orcaella heinsohni) and the Indo-Pacific humpback dolphin (Sousa chinensis) are both listed as Near Threatened (IUCN 2008). In contrast to that classification status for these species, five Irrawaddy dolphin (Orcaella brevirostris) subpopulations of the Mahakam River, Malampaya Sound, Mekong River, Songkhla Lake, Ayeyarwady River (Hilton 2000, Kreb and Budiono 2005) and the eastern Taiwan Strait humpback dolphin subpopulation were 161

184 recently classified as Critically Endangered. The Maui s dolphins, the Black Sea bottlenose dolphin and the Short-baked common dolphins in the Mediterranean Sea are the only other regional populations of dolphins listed as threatened in the IUCN Red List (Birkun et al. 2004; IUCN 2008, Harrison et al. 2009). In the Capricorn Coast region snubfin dolphins comprise a single population located at the southern limit of the species worldwide range, apparently isolated from other coastal Australian populations. The humpback dolphin regional population is subdivided into three sub-populations (Port Curtis, Keppel Bay and Northern Region). Two humpback dolphin sub-populations, Port Curtis and Keppel Bay are under severe anthropogenic stress (see Chapter 3). In addition to the already high level of industrial activity in the area, there are applications lodged for substantially increased industrial activity in both regions. Despite its remoteness, significant disturbance also occurs in the Northern Region, as most of the coastal habitat is part of a military training area. Therefore, an assessment of the threat status of humpback and snubfin dolphin sub-populations in Capricorn Coast is required. As a result of lack of data and long term studies, in order to assess the conservation status of humpback and snubfin dolphins in the Capricorn Coast, it is possible to make reasonable inferences only under categories B and D. It is not possible to assess whether humpback and snubfin dolphin sub-populations met the criteria under the IUCN Red List categories A, C and E, as these require information of negative trends in population size (A), continuing declines in numbers of mature individuals concurrent with small population size (C), or specified probabilities of extinction (E). Population trends in Australian humpback and snubfin dolphins are extremely difficult to detect unless changes in population size are very high (>20% p.a.). At such high levels of annual change, local populations of snubfin and humpback dolphins could have decreased to very low levels by the time any trend is detected (Parra et al. 2005). The criteria for category B specify threshold values for extent of occurrence (CR: <100 km2; EN: <5000 km2; VU: <20,000 km2) or area of occupancy (CR: <10 km2; EN: <500 km2; VU: <2000 km2) in addition to criteria that assess population isolation, fragmentation, decline or fluctuations. Criteria for category D specify threshold values for estimates of the number of mature individuals. The threshold values for classification under category D (CR: <50; EN: <250; VU: <1000) are lower than for category C (CR: <250; EN: <2500; VU: <10,000) because assessment under category C requires additional evidence of a continuing population decline (IUCN 2001). 162

185 8.3.2 Classification of the Capricorn Coast humpback dolphin sub-population under the IUCN B and D categories Analysis of social structure indicates that humpback dolphins in the Capricorn Coast are subdivided into three distinct social units, with higher levels of interaction among individuals within the same community rather than with individuals from different community (Chapter 4). Representative ranges estimated at 95%UD did not show any overlap among communities ranges. However, the three communities are not completely isolated as some individuals were recorded in more than one core region. High gene flow was recorded among samples from Port Curtis and Keppel Bay suggesting that these two social units are part of the unique population. No samples from the Northern Region were able to be collected, however if social structure is correlated to gene flow it is probable that the PC, KB and NR dolphins form a unique regional population (Chapter 5). The study of molecular variance in microsatellite loci has revealed that there is very limited interchange between the Capricorn Coast and the Great Sandy Strait humpback dolphin populations (Chapter 5). Preliminary results including samples collected from Cleveland Bay, North Queensland, (Guido Parra personal communication) provided similar results, suggesting that the Capricorn Coast population is genetically isolated. However, samples of other unsampled areas along the Queensland coast that are currently being collected, such as the Whitsundays Coast, are needed to clarify the population structure of humpback dolphins along the Queensland coast. To assess the population with reference to the IUCN Red List criterion D, population estimates for each humpback dolphin community were done distinguishing among mature and immature individuals using a two groups open population model (see Chapter 6). Abundance of mature snubfin dolphins was estimated by adjusting the population size of marked mature individuals with the proportion of non marked mature individuals. An estimate of the total mature population size and relative CV suggested that there were about 178 mature individuals (CV = 3%; 95% CI: ) in the population, and therefore this sub-population can be classified as Endangered under Criterion D (IUCN 2001). This classification is robust to uncertainty in the estimates of abundance, given that the 95% confidence intervals for the abundance estimate did not exceed 250 dolphins. Furthermore, this estimate is likely to overestimate the real mature population size as 163

186 some individuals were sighted in more than one sub-population and were therefore counted more than one time. There was insufficient information on the geographic range of humpback dolphins along the Capricorn Coast and in particular from surrounding areas to infer a likely threat classification on the basis of criteria B1 (extent of occurrence). The minimum extent of occurrence of the population is approximately 1500 km 2, representing the sum of the representative ranges for each of the three communities. A reasonable upper estimate of extent of occurrence for the population was determined using the Minimum Convex Polygon for the entire dataset. Under this scenario, the extent of occurrence is approximately 2250 km 2. This estimate is likely to overestimate the extent of occurrence as it includes offshore areas where humpback dolphins were never seen. While these estimates are approximations, at both extremes the population is well within the range of extent of occurrence values (100 to 5000 km 2 ) for classification as Endangered under Criterion B1 (IUCN 2001). However, only one sub-criterion (a) out of three sub-criteria listed under criteria B1 could be met, and therefore humpback dolphins from the Capricorn Coast cannot be classified as Endangered" under this criterion. As stated above, the Capricorn Coast humpback dolphin population was estimated to contain fewer than 250 mature individuals. If the results from the analysis of genetic samples collected from other more recently sampled areas support these preliminary results, which suggest that the Capricorn Coast humpback dolphin population is genetically isolated, then the Red List Category Endangered defined under criteria D should be adopted unaltered. If, on the other hand, conspecific populations outside the region are judged to reduce the regional extinction risk, the regional Red List Category should be downgraded to a Vulnerable status. At this stage the level of information available on humpback dolphins in the Capricorn Coast, is too limited to support an assessment of the conservation status at regional level. Nevertheless the small population size recorded in such large area should raise deep question on the long term survival of humpback dolphins in this area and therefore a precautionary approach to increase the level of protection should be considered Classification of snubfin dolphins under the IUCN B and D categories Analysis of distribution of sightings indicates that the Keppel Bay population of Australian snubfin dolphins is geographically and demographically isolated from conspecific 164

187 populations outside the region (Chapters 3, 4 and 7). This hypothesis is corroborated by the genetic evidence presented in Chapter 5. Therefore, there is enough evidence to indicate that the Keppel Bay snubfin dolphin population should be considered as a distinct sub-population as per the IUCN definition. Under this scenario, the IUCN Red List Criteria can be used for the assessment of regional populations without modification. The Keppel Bay snubfin dolphin population was estimated to contain substantially fewer than 250 mature individuals, and therefore can be classified as Endangered under Criterion D (IUCN 2001). This classification is robust to uncertainty in estimates of abundance and the proportion of mature individuals in the population, given that the 95% confidence intervals for the abundance estimate that included all individuals did not exceed 100. Similar to the humpback dolphin case, there is insufficient information on the geographic range of the Keppel Bay snubfin dolphins to infer a threat classification on the basis of criterion B2 (area of occupancy). The minimum extent of occurrence of the population is approximately 470 km 2. A reasonable upper estimate of extent of occurrence for the population is the entire inshore waters of Keppel Bay that extend until Coorio Bay in the north. Thus, the extent of occurrence is approximately 608 km 2. Under this scenario, the population is well within the range of extent of occurrence values for classification as either Critically Endangered or Endangered under Criterion B2 (IUCN 2001). However, none of the sub-criteria were met and therefore the Keppel Bay snubfin dolphin population cannot be assessed under this criterion. However, considering 1) the ecological importance of this population, being the southernmost population of snubfin dolphins, 2) that in both criteria, mature population size and area of occupancy, are very close to the upper limit required for the Critically Endangered status, 3) the high level of anthropogenic disturbance that this population will face in the near future, and 4) that this population is not only geographically isolated but possibly also genetically isolated; a Critically Endangered status may be more appropriate Future research Data on population trends and population structure are the two major information gaps needing to be addressed to clarify the conservation status of humpback and snubfin dolphins at local, state and federal levels. Therefore, the continuation of photo- 165

188 identification studies in the Capricorn Coast is needed to provide valuable information on abundance and longer-term trends in population abundance for humpback and snubfin dolphins in this region. Furthermore, long term photo-identification studies will enable more accurate data on social structure, distribution, home range, movement pattern and life history parameters to be obtained for these humpback and snubfin dolphins Final remarks The paucity of data on humpback and snubfin dolphins is clearly a barrier to formal status assessment using IUCN criteria. Coastal surveys to obtain more data on current abundance, extent of occurrence, population trends and genetic population structure along the Capricorn Coast and other regions in Central Queensland are underway. The primary aim is to answer the existing information gaps and to provide stronger evidence to support the review of the status of humpback and snubfin dolphin along the Capricorn Coast, in Queensland and in Australia. The results of this study provide evidence of small population size, geographic and genetic isolation. This information coupled with the high level of anthropogenic impacts that a large section of the Capricorn Coast will face in the near future should be sufficient to support arguments to increase the level of protection for humpback and snubfin dolphins in the Capricorn Coast. Compared to Cleveland Bay where the current level of protection offered to snubfin and humpback dolphins is relatively good (Parra et al 2005), in the Capricorn Coast the level of protection to inshore dolphins is low. Most of the area used by both species, such as the entire Port Curtis study area, Fitzroy River and estuary, and Port Clinton are not part of the Great Barrier Reef Marine Park, and are not part of state marine parks. Most of the remaining area is a general use zone with low level or no formal protection. Gladstone Port in Central Queensland will soon undergo a substantial expansion becoming one of the largest coal exporting ports in the world. A new liquefied natural gas (LNG) facility will be built by Shell CSG (Australia) Pty Ltd on Curtis Island. The LNG facility is expected to produce up to 16 Mtpa (metric tonne per year), involving phased construction of up to four LNG trains or processing plants. A proposed gas pipeline from the Gladstone City Gate to Curtis Island will supply gas to the LNG plant where it will be processed, cooled and stored in LNG tanks for subsequent loading onto LNG carriers via a jetty and export to international markets. Associated with the expansion of port facilities, the Gladstone Ports Corporation (GPC) will undertake dredging to extend, deepen and 166

189 widen the existing channels and to create new swing basins, and berth pockets to double the port capacity. In addition to the proposed LNG projects and the announced staging of the Wiggins Island Coal Terminal, a number of other large industrial developments are also planned in the region. A similar plan is expected to occur in the Fitzroy River estuary. A new coal port facility with associate dredging and widening of an existing natural channel is expected to be built on Balaclava Island by Xtrada. It will be designed to enable up to 35 million tonnes per annum of coal via vessel of up to 110,000 tonnes capacity. This project involved the development of a new shiploading facility on Balaclava Island, the creation of new swing basin and a new ship channel. The impacts of both developments on inshore dolphins have not been adequately considered. Considering the high level of noise pollution, water pollution, altered habitat and increased vessel traffic and operations in areas frequently use by both species, the risks of local extirpation should not be underestimated. Population viability analysis of well known coastal dolphin species (i.e., bottlenose dolphin, Tursiops truncatus, and Hector s dolphin, Cephalorhynchus hectori) indicates that populations of less than a hundred animals face very high extinction probabilities (Thompson et al., 2000; Burkhart and Slooten, 2003). As both developments are planned to start by 2011 or 2012, and no mitigation actions have been considered to minimise the impact of humpback and snubfin dolphins in the area, a monitoring project is needed to assess and mitigate the impacts of the developments on these dolphins. Our low estimates raise concerns about the long-term survival of both species in this local region and emphasizes the need to increase research and conservation efforts in Australia if conservation is to be successful. 167

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216 Appendices Appendix 1. PCR protocol and results for genetic sexing of humpback and snubfin dolphins samples. LOCUS: Sexing ID Sex ID Sex PCR ID: Sexing M F PCR machine: M M PCR setup: Conc. per rxn [ l] cocktail [ l] F M Template [ l] F F Primer ZFX F F F Primer ZFX R F M Primer SRY F F F Primer SRY R F F dntps [mm] F F MgCl 2 [mm] F M buffer (10x) F M JumStart Taq 5U/µL F M ddh 2 O [ l] F M final volume [ l] F F PCR profile: Temperature Time [min] No. of repeats M F Initial Denat. [ o C] 94 1` M F Touchdown F F Touchdown F F Denaturation [ o C] `` M F Annealing [ o C] `` 40* M F Extention [ o C] 72 60`` M F Fin. Extent. [ o C] 72 10` M M M F M? F M M M M M F M M F F F M M F F M F F F F M M F F F F F F M 21083? M F 194