Abundance, Population Dynamics, and Social Structure

of Bottlenose Dolphins (Tursiops truncatus) in the Bay of

Islands, New Zealand

Olivia Nicole Patricia Hamilton

A thesis submitted in partial fulfilment of the requirements for the degree of Master of

Science in Biological Sciences

The University of Auckland

2013

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Dolphins foraging in the Bay of Islands.

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Abstract

Obtaining estimates of abundance and understanding the demographic factors that cause

change in abundance for wildlife populations is an important task for conservation

biologists. This is particularly true when the animal under study is highly social, as the

loss of individuals may directly impact the population’s social structure.

The purpose of this study was to assess the current population status and social structure

of bottlenose dolphins (Tursiops truncatus) in the Bay of Islands, New Zealand. Boat-

based surveys were conducted to photo-identify individuals and collect demographic data

from an independent research vessel between February and December 2012. Group size

ranged from 3-28 dolphins (median = 25), groups containing calves were not significantly

larger than those without calves, and the population was mainly composed of adults

(95%). A total of 56 individuals were photo-identified, ten new to the Bay of Islands

photo-identification catalogue in 2012, with an average of 5 sightings per individual.

Robust design mark-recapture models were used to estimate abundance, apparent survival

and temporary emigration rates from photo-identification data collected in 2009 and in

2012. Apparent survival was estimated at 0.63 (95% CI, 0.53-0.72) and abundance

estimates fluctuated from a low of 24 (February 2012: 95% CI: 24-24) to a high of 94

(Demember 2009: 95% CI: 84-105). Temporary emigration patterns were Markovian,

which is in contrast to prior research, where temporary emigration patterns were random.

A low apparent survival rate indicated that a number of dolphins had permanently

migrated from the bay during 2009-2012, suggesting a shift in habitat use during the study

period. Small abundance estimates and Markovian emigration pattern indicate that small

number of dolphins use the bay regularly, while many others only visit occasionally.

Social analysis was carried out using the program SOCPROG to determine the strength

and stability of associations between individuals and identify whether associations were

sex-specific. Individuals were found to associate in a non-random manner without

preference to sex. The population is characterised by two levels of associations: short-term

acquaintances and long-term companionships. Long lasting associations were found

across sexes. Fine-scale changes in association patterns have occurred, which is probably

due to changes in the population size and individual residency patterns. The shift in habitat

use and changes in association patterns suggests the bay is a less important part of the

range for a number of dolphins. The observed changes in association patterns are most

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likely a consequence of the decline in population size; a number of social units have been

fragmented due to a shift in habitat use.

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Acknowledgments

There are a number of people who have helped me along the way in the last year and

without them this thesis would not have been completed.

The first person I want to thank is my supervisor, Rochelle Constantine. I really could not

imagine a better person to be guided by. Thanks for your support, advice, patience, and for

giving me the room to think independently. I have not only gained invaluable skills, but

the last year has most definitely been the highlight of my academic career and I am very

grateful for that.

I was lucky enough to be guided by a number of other fantastic people over the last year,

which undoubtedly improved the quality of this thesis. In particular I want to thank

Lyndon Brooks (Southern Cross University) for helping me along the way with the mark-

recapture work; I am very lucky to have had such a great statistician on my side. A big

thanks goes to Delphine Chabanne (Murdoch University) and Lars Bejder (Murdoch

University) for so kindly letting me come over to Perth (again thanks Rochelle!) for a

crash course in SOCRPROG. Big thanks to Emma Caroll for being my surrogate

supervisor while Rochelle was away. Bhakti Patel thank you for driving me around out on

the water every day and putting up with my stress levels. You are a legend!

Finding dolphins can be very hard work, especially in such a large area as the Bay of

Islands. A big thanks goes out to the tour boat operators for keeping me updated on the

dolphin’s whereabouts. Thanks to you, my time was much better spent collecting data.

To my mum, Vicki Hamilton, thank you not only for your support over the past six years,

but throughout my entire life. Thank you for believing in me, pushing me along the way,

putting up with my moods, feeding me, letting me practice my presentations a million

times in a row to you, and the list goes on. You are a truly amazing person and I owe a

huge portion of this thesis to you.

To my sister, Alex thank you for looking after me in so many shapes and forms, even from

all the way across the ditch. Thank you for being my rock, for believing me and for

cheering me along from the side-lines. Jon, thank you for your support, kindness and also

looking after me!

To my dad, Richard, this thesis is partly dedicated to you. Thank you for looking after me

from wherever you may be now. I miss you lots.

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Not only do I owe my family, but also my amazing friends. If I were to thank every single

one of my friends that has helped me in some shape or form over the duration of this study

we would be here forever. So a big thank you to every one of you who has help me – you

know who you are. A special thank you goes to Diana Davies. Thank you for always

being there for me every step along the way! I really could not have done this without you.

I have been spoilt rotten by so many of my other friends. A special shout out to Katie

Walton, Emily Moon, Madeleine Healy, Brian Ansell, Andrew Fava and Mark Parsonage

– thank you for saving me from the student life. I have managed to evade eating the likes

of baked beans on toast for dinner for most of the year because of your generosity. Thanks

goes to Danny Rawlins for helping me with Illustrator. Thank you to my A-Team for

being so supportive over the past six years. Mary Hilsz, thank you for looking after me in

Perth!

Last, but definitely not least, to the dolphins in the Bay of Islands, thank you for letting me

tag along with you, learn from you, and for inspiring me. It has undoubtedly been the best

experience of my life.

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Table of Contents

Abstract ............................................................................................................................... iv

Acknowledgments .............................................................................................................. vi

List of Figures ..................................................................................................................... xi

List of Tables ...................................................................................................................... xi

1 Introduction ............................................................................................ 1

1.1 Population Biology ................................................................................... 1

1.2 Habitat use ................................................................................................ 2

1.3 Habitat selection ....................................................................................... 3

1.4 Sociality .................................................................................................... 5

1.4.1 Social groups 5

1.4.2 Evolution of group living 5

1.4.3 Social structure 7

1.5 Conservation ............................................................................................. 8

1.6 Bottlenose dolphins .................................................................................. 9

1.6.1 Ecology 10

1.6.2 Social structure 11

1.6.3 Association patterns: 11

1.6.4 Residency patterns 12

1.6.5 Group size 12

1.6.6 Bottlenose dolphins in New Zealand 13

1.7 Thesis aims and objectives ..................................................................... 14

2 Abundance, Survival and Temporary Emigration ............................ 16

2.1 Introduction ............................................................................................ 16

2.1.1 Mark-recapture methods 16

2.1.2 MR models 17

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2.1.3 Study population 20

2.2 Methods .................................................................................................. 21

2.2.1 Study site 21

2.2.2 Boat surveys 22

2.2.3 Photographic data analysis 23

2.2.4 Data organisation 26

2.3 Statistical analysis .................................................................................. 27

2.3.1 Model assumptions 27

2.3.2 Closed Robust Design 29

2.3.3 Mark rate 30

2.3.4 Total population size 31

2.4 Results .................................................................................................... 32

2.4.1 Survey effort and data sets 32

2.4.2 Photographic data analysis 33

2.4.3 Goodness of Fit tests 33

2.4.4 Closed Robust Design 34

2.5 Discussion ............................................................................................... 37

2.5.1 Capture probabilities 37

2.5.2 Estimates of survival 38

2.5.3 Temporary emigration 40

2.5.4 Estimates of abundance 42

2.5.5 Limitations 44

2.5.6 Summary 44

3 Social Structure .................................................................................... 46

3.1 Introduction ............................................................................................ 46

3.2 Methods .................................................................................................. 49

3.2.1 Surveys and photo identification 49

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3.2.2 Group size and age class composition 49

3.2.3 Photographic analysis 50

3.3 Data analysis ........................................................................................... 50

3.3.1 Accuracy of social representation 51

3.3.2 Association indices 51

3.3.3 Preferred or avoided associations 52

3.3.4 Temporal patterns of associations 53

3.4 Results .................................................................................................... 54

3.4.1 Group size and age class composition 54

3.4.2 Accuracy of social representation 56

3.4.3 Association indices 56

3.4.4 Preferred associations 57

3.4.5 Standardised lagged association rates 60

3.5 Discussion ............................................................................................... 63

3.5.1 Group size and demographic structure 63

3.5.2 Social structure 65

3.5.3 Summary 68

4 General Discussion ............................................................................... 70

4.1 Main Aims .............................................................................................. 70

4.1.1 Abundance, survival and temporary emigration70

4.1.2 Group dynamics and social structure 71

4.2 Conservation and future research ........................................................... 72

References .......................................................................................................................... 76

Appendices ......................................................................................................................... 97

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List of Figures

Figure 2.1 The Bay of Islands. The study was conducted within the harbour boundary,

which lies between Ninepin and Piercy Islands. Green represents land and dark blue

indicates deeper water (Hartel, 2010). ................................................................................ 22

Figure 3.1 Distribution of group sizes of bottlenose dolphins in the Bay of Islands in 2012

(n=15). ................................................................................................................................. 55

Figure 3.2 Distribution of the Half Weight Index (HWI) of assocation for bottlenose

dolphins in the Bay of Islands. Notations: All = HWI between all individuals, F-F =

female-female HWI, M-M = male-male HWI, F-M = females-males HWI……………..57

figure 3.3 Standardised lagged association rates (SLAR) for: (a) all individuals; (b) among

females; (c) between females and males and; (d) among males. Each SLAR is compared to

the null association rate: red for (a) and green for (b, c, d). The best-fitted model for all

individuals in (a) is represented by a green line. ................................................................ 62

List of Tables

Table 2.1 Scale of photo quality and attributes used to evaluate the photo quality of

sighting 2.1 data for bottlenose dolphins in the Bay of Islands. Adapted from Tezanos-

Pinto(2009)……………………………………………………………………………….25

Table 2.2 Scale of nick distinctiveness based on a system devised by Urain et al (1999).

D1 represents individuals with very distinctiveness markings, D2 with moderate markings

and D3 with markings that contain little information. ........................................................ 26

Table 2.3 Summary of photo-identification effort conducted in the Bay of Islands from

2009-2010 (Hartel, 2010) and 2012. ................................................................................... 32

Table 2.4 Summary of the RD for each primary session from 2009-2012. The CAPTURE

model selection criterion (MSC, as implemented by MARK; White & Burnham, 1999)

was used to evaluate the most appropriate closed model given the data. = time

variation in capture probability and a behavioural response to first capture; = a

behavioural response to first capture; = time variation in capture probabilities and; =

equal probability of capture for all dolphins. ...................................................................... 33

Table 2.5 Results from Goodness of Fit tests run in U-CARE for the CJS open model.

Overall, Test 3 looks for violations of the assumption that all marked animals have the

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same probability of surviving between occasions. Test 2 looks for violations of the

assumption of equal catchability. A significant Test 2.CT test indicates that there was a

trap response to first capture. A significant result in Test 3.SR indicates that a significantly

large number of animals were only seen once (i.e. tests for transience). The test statistic

for both Test 3.SR and Test 2.CT presented. ...................................................................... 34

Table 2.6 RD models fitted to the capture histories of bottlenose dolphins the the Bay of

Islands to estimate parameters for population size, survival, emigration and capture

probability, which were allowed to vary with time both within and between sessions.

Phi=apparent survival; g”=probability of temporary emigrating off the study site;

g’=probability of remaining a temporary emigrant p=probability of capture. Where (.) =

constant between sessions; (s) = varies between primary sessions and; (t) = varies within

sessions. Markovian temporary emigration = g”,g’; random temporary emigration = g and;

no g parameter is found in no movement models. In all models, recapture probabilities

were set to equal capture probabilities c=p. ........................................................................ 36

Table 2.7 Abundance estimates of distinctly marked individuals and corrected abundance

estimates taking into account the proportion of unmarked dolphins in the Bay of Islands

for the best fitting model (constant survival, constant Markovian temporary emigration

and full time variation in capture probabilities). = abundance estimate of marked

individuals; SE = standard error; =total abundance estimate. .................................. 37

Table 3.1 Definitions of the four relative age classes for bottlenose dolphins (Constantine,

2002). .................................................................................................................................. 50

Table 3.2 Summary of the group dynamics for bottlenose dolphins in the Bay of Islands

in 2009 (Hartel, 2010) and 2012. The brackets contain the interquartile range. ................ 55

Table 3.3 Average and maximum Half Weight Indices (HWI) and standard deviations

(SD) between and within sex classes for bottlenose dolphins in the Bay of Islands. ......... 57

Table 3.4 Observed and random Half Weight Indices (HWI ) ± standard deviation (SD)

and P-values are indicated for the random association test. The test statistic was the SD; P-

values < 0.05 indicate that the observed SD was signficantly higher than the random data.

............................................................................................................................................. 58

Table 3.5 Dyads that had significantly strong associations (>0.5, p<0.05) compared to

random permutations. Only dyads with a HWI of 0.5 or more are displayed. Colours

indicate the time period for which the individual has been classified as a core user.

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Red=1996-2010; blue=2003-2010; grey=2007-2010; yellow=1996-2000; green=1996-

2005; purple=2003-2005. .................................................................................................... 59

Table 3.6 Fit of four social-system models to the standardised association rate (SLAR) for

bottlenose dolphins in the Bay of Islands. Notation: CC = constant companions; CA =

casual acquaintances. The value in bold type indicates the best fit model with the lowest

Quasi-likelihood Akaike’s Information Criterion QAICc value. ........................................ 61

1

1 Introduction

1.1 Population Biology

A population is defined as a group of individuals of a single species inhabiting a specific

geographical area (Molles, 2010). The interaction between animals and their environment

as well as other organisms ultimately shapes both their distribution and abundance

(Molles, 2010). However, abundance and distribution are not static but instead they are

often in flux both spatially and temporally as a result of changes in one or more of the four

fundamental population parameters common to all species; birth, death, immigration and

emigration (Anderson, 1974).

Understanding the factors that explain changes in population size are of primary interest to

ecologists for both theoretical and applied reasons (Ranta et al., 1995). One of the greatest

theoretical debates in ecology is the relative importance of density dependence and density

independence in the regulation of populations (Bonsall et al., 1998). Density dependence

occurs when the per capita growth rate is a function of the populations own density; an

increase in population size past a certain threshold leads to an increase in mortality rate or

lower reproductive output (Sinclair & Pech, 1996). Density dependent factors that cause

mortality or reduce fecundity include intra- and interspecific competition and predation

(Sinclar & Pech, 1996). For density independence, the per capita growth rate is

independent of population size, and causal factors are related to changes in environmental

variables. It is now generally accepted that density dependence and density independence

are not mutually exclusive (e.g. Sæther, 1997), and irrespective of this debate they both

imply the same process; that there is some mean level of density around which a

population fluctuates, and the population does not stray far away from this level (Turchin,

1995). It is important to note that density dependent and density independent factors do

not affect all members of a population evenly, but rather there are often cohort and sex-

specific effects, which leads to variation in vital rates (i.e. survival, fecundity) within a

population (Coulson et al., 2001).

The movement of animals also has a profound influence on the population dynamics, and

thus its abundance (Bowler & Benton, 2005). A number of species permanently emigrate

from their natal ground, often motivated by factors such as to increase mating success,

avoid inbreeding (Pusey, 1987) and establish new territory (Smith, 1993). In contrast,

movements in and out of an area may be temporary. Temporary emigration is often

relative to the study area; location and size of a study site rarely cover the entire area in

2

which animals move, especially wide-ranging mammals. Temporary movement is unlikely

bring the population size away from the mean over the long term. However, if permanent

emigration is not compensated for (i.e. immigration, birth) then local extinction becomes

inevitable (Sjögren, 1991).

It has become increasingly more important to assess population parameters for

conservation purposes. Anthropogenic activities interfere with the natural regulation

processes, which often has detrimental consequences. For example, in the Crozet Islands,

wandering albatross Diomedea exulans underwent a marked decline in abundance. They

identified that high mortality was the cause as opposed to low reproductive output, and

from this they could conclude that accidental deaths in fishing tackle and deliberate

culling by fisherman were most likely the cause (Weimerskirch & Jouventin, 1987). This

highlights the importance not only assessing population size, but also determining which

vital rates have been altered in order to guide management decisions.

1.2 Habitat use

The habitat of a species is any place where an organism is able to live (Fretwell & Lucas

Jr, 1969). Abiotic conditions related climate and the physical environment set the

physiological boundaries of organisms and therefore define its habitat at a broader scale

(Soberón & Peterson, 2005). Vital resources, such as food and mates, which are required

to maintain viable populations, are distributed within these boundaries (Begon et al.,

1986). However, within an animal’s habitat, resources are rarely uniformly distributed but

more typically exist as a mosaic of patches due to environmental heterogeneity (Ballance,

1992). This in turn has a profound influence on the spatial patterning of animals; they are

usually not spaced randomly but instead closely match resource distribution both spatially

and temporally (Ballance, 1992; Boyce & McDonald, 1999). This pattern defines habitat

use, which explains the distribution of animals relative to habitat features (e.g. resources,

nesting sites) (Bergin, 1992).

While not mutually exclusive, there are three main habitat-use strategies that animals

employ. They represent different solutions to a common goal; to secure high quality

habitat, which in turn, increases fitness. The first is migration, which is defined as the

periodic movement of animals from one place to another (Lockyer & Brown, 1981).

Movements are quite often seasonal, and involve moving over large geographical

distances (Lockyer & Brown, 1981). For example, humpback whales Megaptera

3

novaengliae exploit the highly productive waters at higher latitudes to feed, while utilising

warm water in sub-tropical and tropical regions to mate and breed (Clapham, 2000). The

second strategy is territoriality. Here, animals actively defend against conspecifics a

portion of their home range that contains valuable resources (McLoughlin et al., 2000;

Taylor, 1988). While territoriality is a common strategy for terrestrial species e.g. spotted

hyena Crocuta crocuta (Boydston et al., 2001), the red squirrel Tamiasciurus hudsonicus

(Dantzer et al., 2012), chimpanzees Pan troglodytes (Sobolewski et al., 2012), there is

little evidence of it for cetaceans. The wide ranging nature of cetaceans, the fluid nature of

their prey, and their three dimensional environment all make territoriality a less attractive

option for cetaceans (Connor, 2000). The third strategy is localised, like territoriality, as

opposed to covering a large geographical span like migration, but in contrast to

territoriality, animals do not actively defend their habitat. This is where animals occupy a

home range, which is defined as the normal area through which an animal travels to carry

out essential activities such as foraging, mating, and caring for young (Burt, 1943).

Coastal cetaceans typically occupy a home range e.g. killer whales Orcinus orca (Baird,

2000), Hector’s dolphins Cephalorhynchus hectori (Rayment et al., 2009). Moreover,

there is often a great degree of variability in home range characteristics for species both

between populations, and between individuals within the same population e.g. bottlenose

dolphins (Connor et al., 2000).

1.3 Habitat selection

Habitat selection describes the preferential use of some habitat patches over others, which

is guided by behavioural decisions or responses (Boyce & McDonald, 1999; Fortin et al.,

2009; Morris, 2003). The process of habitat selection is complex as it occurs at a number

of different spatial scales (i.e. biogeographic through to territorial range) (Bergin, 1992;

Mysterud & Ims, 1998). This is due to environmental heterogeneity that results in spatio-

temporal hierarchical organisation of the environment (Allen & Starr, 1982). As a result,

identifying which scale to use when analysing an organism’s habitat selection can be

difficult and comparisons between studies of habitat selection are not always possible

(Mayor et al., 2009). There are a number of factors that influence habitat selection. Food

acquisition has received the most attention as energy intake optimises growth, survival,

and reproduction and therefore, largely impacts fitness. Many studies have indeed

provided evidence that food distribution affects habitat choice both spatially and

temporally e.g. mountain gorillas Gorilla gorilla beringei (Vedder, 1984), moose Alces

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alces (Bjørneraas et al., 2012), bison Bison bison (Fortin et al., 2002), red deer Cervus

elaphus (Langvatn & Hanley, 1993) vicuña Vicugna vicugna (Mosca Torres & Puig,

2011), grey seals Halichoerus grypus (Thompson et al., 1996), and Hector’s dolphins

Cephalorhynchus hectori (Bräger et al., 2003). However, there are a number of other

important factors that influence habitat selection and in many cases animals must trade-off

foraging in areas of high quality food to accommodate for these (Gordon & Wittenberger,

1991).

Predation risk is a major factor influencing habitat selection and many studies have shown

that animals will sacrifice utilising high quality feeding patches for safety e.g. pied

cormorants Phalacrocorax varius (Heithaus, 2005), baboons Papio cynocephalus ursinus

(Cowlishaw, 1997) and dugongs Dugong dugon (Wirsing et al., 2007). This food-safety

trade-off may not affect members of a population uniformly but instead responses may be

age- and sex-class dependent e.g. bottlenose dolphins Tursiops truncatus (Heithaus &

Dill, 2002) and moose (Bjørneraas et al., 2012).

There are also a number of other environmental and biological factors that influence

habitat selection. Animals may alter their movements to avoid harassment e.g. insect

harassment of reindeer Rangifer tarandus tarandus (Hagemoen & Reimers, 2002); human

harassment of bottlenose dolphins (Allen & Read, 2000; Bejder et al., 2006b);

interspecific competition e.g. birds (Robinson & Terborgh, 1995); and intraspecific

competition e.g. shrews, mice and voles (Adler, 1985). Physiological constraints may also

be important for a range of species. Barton et al. (1992) found that habitat selection for

desert baboons (P. anubis) was affected both spatially and temporally by proximity to

watering holes suggesting that thermoregulatory requirements constrain habitat choice.

(Elliott et al., 2011) suggested that for bottlenose dolphins in Doubtful Sound, New

Zealand, habitat choice was not affected by foraging opportunities but instead groups may

avoid certain areas of their range in cold months to minimise thermal stress on calves.

Determining what influences habitat selection by cetaceans is challenging as observations

are limited by the marine environment. Habitat selection by delphinids is often studied by

comparing their distribution in relation to environmental characteristics (Bräger et al.,

2003). Environmental characteristics include water depth, bottom topography, thermocline

depth, salinity and distance from the shore (Bräger et al., 2003). Environmental

characteristics may affect habitat selection directly e.g. thermoregulation and energetic

demands (Wilson et al., 1997), or indirectly by influencing prey distribution, predator

5

avoidance or facilitation of social interactions (e.g. Heithaus & Dill, 2002; Heithaus &

Dill, 2006; Mann et al., 2000; Wells et al., 1980).

1.4 Sociality

1.4.1 Social groups

For all sexually reproducing organisms, nearby conspecifics are an integral part of the

environment (Whitehead, 2008a). However, a fundamental change in the relationships

between conspecifics occurs when individuals begin to cooperate with one another and

live in groups. The grouping nature between animals is diverse, and therefore it is difficult

to find a general definition of a social group that is meaningful for all taxa (Krause &

Ruxton, 2002). There has been emphasis on the importance of spatio-temporal proximity

between individuals as a fundamental criterion; the ability to communicate and thus

transfer information can most reliably be achieved when individuals are in close

proximity. However, close proximity is a relative term as it depends on the

communication channels of a particular species. For example, some baleen whales can

communicate over long distances in order to find mates (Tyack, 2000). Many groups form

independently of any benefits individuals may receive from others. These are called

aggregations and often form where environmental conditions are favourable or where

resources are concentrated (Connor et al., 2000; Eisenberg, 1966). Therefore, perhaps the

most important criterion for defining a social group is that they are brought together by

social attraction (Krause & Ruxton, 2002). This is what Connor (2000) describes as

mutualistic group formation; where individuals actively seek conspecifics and join a group

because of the potential benefits they can receive from others.

1.4.2 Evolution of group living

In general terms, the evolution of sociality can be explained in terms of the relative

benefits and costs associated with group living; only when the benefits gained outweigh

the costs to an individual (Connor, 2000; Eisenberg 1966; Krause and Ruxton, 2002). The

potential costs to group living are common to all taxa (Alexander, 1974) as they arise from

density dependent intra- and interspecific interactions. These costs include increased

intraspecific competition, disease and parasite transmission, and detectability by predators

(Cote & Poulin, 1995; Krause & Ruxton, 2002; Janson & Goldsmith, 1995; Wrangham et

al., 1993).

There are also a number of benefits to be gained from group living. However, unlike the

costs, the relative importance of the benefits varies between species, and between

6

populations of the same species, depending on their environmental settings (Connor, 2000;

Whitehouse & Lubin, 2005). Due to the accessibility of terrestrial animals, literature

surrounding the benefits of sociality is largely centred on terrestrial systems (e.g. Cook &

Cartlan, 1966; Macdonald, 1983; Wittemyer & Getz, 2007). In contrast, studies of

cetaceans have only really started to take off in the last thirty years (Connor et al., 2000).

Since then, the number of examples where researchers have investigated the benefits of

group living for social marine animals has increased (Connor et al., 1998; Connor et al.,

2000;).

A major benefit of group living is that animals can maximise their foraging efficiency or

food intake through communal hunting (MacDonald & Kays, 1998) such as for African

wild dogs Lycaon pictus (Creel & Creel, 1995) and transient killer whales (Baird & Dill,

1996). Cooperative defence of territory promotes group formation for a range of terrestrial

mammals and birds including coyotes Canis latrans (Bekoff & Wells, 1986); African wild

dogs (Creel, Creel & Monfort, 1998); lions (Mosser & Packer, 2009); and the Mexican jay

Aphelocoma ultramarine (Brown, 1963). The need to defend food from both conspecifics

(e.g. primates; Wrangham, 1980) as well as other species (e.g. African wild dogs

protecting kills from spotted hyaenas; Fanshawe & Fitzgibbon, 1993) has also had strong

influence on group formation for a number of species. Decreased risk of predation through

increased predator detection, the dilution effect, and creating confusion for the predator

provide another major evolutionary force for group living (Dehn, 1990; Hamilton, 1971;

Schaik et al., 1983; Treves, 1999). There are also a number of reproductive benefits to be

gained. For example, male lions, cheetahs Acinonyx jubatus, chimpanzees, baboons and

bottlenose dolphins form male-male coalitions which enable them to better compete for

females (Bercovitch, 1988; Caro, 1994; Connor et al., 2001; Packer & Pusey, 1982; Scott

et al., 2005; Watts, 1998). Finally, indirect benefits in the form of inclusive fitness (i.e.

increase ones genetic success by assisting close relatives raise their offspring) can be

gained through caring for relatives offspring. This is thought to explain cooperatively

breeding birds such as the Seychelles warbler Acrocephalus sechellensis (Komdeur,

1994). Alloparental care observed in the partially matrilineal sperm whale (Physeter

macrocephalus) (Gero et al., 2009; Whitehead, 1996). However, while molecular work

has shown that kinship is high within groups (Richard et al., 1996) and that preferred

relationships within sperm whale units correlate with relatedness (Gero et al., 2008),

whether or not adults and the young they babysit are related is unknown.

7

1.4.3 Social structure

Animal social structures exist on a continuum from solitary, where individuals only come

into contact for the breeding season, through to complex social systems in which

relationships between individuals are stable (Eisenberg, 1966). Understanding the social

structure of a population is crucial as it influences a number of facets of its biology

including dispersal and gene flow (Singleton & Hay, 1983; Swedell et al., 2011),

information transfer and cultural transmission (Rutz et al., 2012), disease transmission

(Cross et al., 2004), population growth rates (Courchamp et al., 1999), and habitat use

(Baird & Dill, 1996; Hoelzel, 1993), and is therefore fundamental for guiding

conservation and management strategies (Sutherland, 1998).

Hinde (1976) developed a conceptual framework for analysing the social structure of a

population, which consists of three interacting levels. The basic unit is the interactions

between individuals. These interactions are defined by their content (what the interactions

are) and quality (how the individuals are doing it). The cumulative effects of the

interactions define the relationships between individuals, which represent the second level

of a social structure. Relationships are not only defined by the content and quality of the

interactions, but also by the temporal patterning of the interactions. Understanding the

relationships between individuals reveals much about the ecological interplays within a

population including competition, cooperation, and dominance (Whitehead, 1997). The

third and final level is the social structure itself, which is a product of the nature, quality

and patterning of the relationships. Unravelling the pattern of the relationship is crucial as

it reveals properties of the population that are not evident from examining the

relationships themselves (Hinde, 1976).

While phylogeny may constrain the social structure of a species, it does not necessarily

determine it (Chapman & Rothman, 2009; Thierry et al., 2000). This is supported by cases

where closely related species e.g. squirrel monkeys, Saimiri oerstedi and S. sciureus

(Mitchell et al., 1991), and different populations e.g. sympatric killer whales along the

Pacific Coast of North America (Baird & Whitehead, 2000) have contrasting social

structures. Similarly, there are a number of examples where distantly related species share

the same social structure. Examples include black faced spider monkeys Ateles paniscus

chamek and chimpanzees (Symington, 1990); and sperm whales and elephants (Whitehead

& Weilgart, 2000). These patterns have emerged largely as a result of

differences/similarities in ecological pressures between environments (e.g. distribution and

availability of resources and predation pressure) which shape social structure in such a

8

way that optimises the benefits, while decreasing costs associated with group living

(Lehmann et al., 2007; Nakagawa, 1998). For example, both bottlenose dolphins and

chimpanzees live in fission-fusion societies where by group composition is dynamic. It

has been suggested that this convergence can be explained by similarities in the patchy

distribution of their resources (Connor et al., 1998). As a result, a flexible social structure

is beneficial as it allows groups to adjust according to the nature of the food patch to

decrease intraspecific competition and thus maximise fitness (Connor et al., 1998).

Clues to the costs and benefits of group living for a species and the ecological

determinants that shape their social structure can be found by examining the social

interactions between individuals (O’Brien, 1991). Moreover, in order to unravel a

population’s social structure, information must be obtained on the interactions between

individuals as they form the basis of the system (Whitehead, 2009). While this is typically

an easier task when studying terrestrial species, the nature of the marine environment

restricts direct observations and therefore severely limits the amount of information

concerning interactions that can be obtained (Chilvers & Corkeron, 2002). As a result,

association patterns between individuals in both space and time are used as a proxy with

the logic that interactions usually occur when individuals are in association (Whitehead,

1997). This has been applied successfully in many studies, allowing for a greater

understanding of social structure for a number of cetaceans (Coakes & Whitehead, 2004;

Gowans et al., 2001; Ottensmeyer & Whitehead, 2003; Tosh et al., 2008).

1.5 Conservation

Globally, the conservation status of a number of species is threatened due to the negative

impacts associated with anthropogenic activities (Kingsford et al., 2009). Factors such as

habitat loss, modification and degradation; the spread of invasive species; over-

exploitation; pollution; and climate change are all implicated in the loss of species

worldwide (Barnosky et al., 2011; Wagler, 2011). While the effects of these activities are

far spread, animals living in habitat holding high economic value (e.g. forest, lowlands) as

well as around areas of development (e.g. coastal areas) are particularly vulnerable.

The conservation status of mammals worldwide is of concern, particularly for marine

mammals. Schipper et al. (2008) reported that approximately 36% of all marine mammals

are threatened with extinction. Moreover, population sizes for a number of marine

mammals, including those in the International Union for Conservation of Nature (IUCN)

9

Least Concerned category are in decline, suggesting that the number of species threatened

with extinction is set to increase in the future (Schipper et al., 2008).

For cetaceans, past exploitation (Baker & Clapham, 2004; Clapham et al., 1999); present

day exploitation for the meat trade (Bowen-Jones & Pendry, 1999), scientific research

(Clapham & Baker, 2002); the live-capture trade (Fisher & Reeves, 2005); coastal

development and degradation (Jefferson et al., 2009); by-catch and vessel strikes (Laist et

al., 2001); chemical and noise pollution (Cardellicchio, 1995; Erbe, 2002); global climate

change (MacLeod, 2009); and tourism (Constantine et al., 2004) threaten the livelihood of

species around the globe, with inshore populations being at greatest risk (Connor et al.,

2000). Once reduced in size, populations become more vulnerable to extinction through

loss of genetic variation, reduction in effective population size, demographic and social

changes, and from stochastic environmental events (Fagan & Holmes, 2006; Rojas-Bracho

& Taylor, 1999; Whitehead et al., 2000).

With this in mind, it is crucial that effective monitoring and management plans are

developed and implemented in order to protect cetaceans worldwide. In recent years, a

number of tools have been developed to assist conservation biology research and

successfully applied to cetacean studies. For example, Geographical Information Systems

(GIS) have enabled researchers to study fine-scale movements of individuals relative to

their environment thereby allowing them to identify areas of critical habitat (e.g.

bottlenose dolphins; Torres et al., 2003). Mark-recapture techniques have been particularly

useful for understanding group dynamics (Kogi et al., 2004), determining how social

structure has been affected by human disturbance (Chilvers & Corkeron, 2001), estimating

abundance (Calambokidis & Barlow, 2004; Read et al., 2003), and detecting trends in vital

rates such as survival (Mizroch et al., 2004).

1.6 Bottlenose dolphins

Bottlenose dolphins (Tursiops spp) are one of the world’s best studied cetaceans as their

coastal distribution makes them particularly accessible to researchers (Connor et al.,

2000). However, as with other cetaceans, field studies are limited as observations are

restricted to the surface activity of animals, which represents only a small fraction of their

lives (Benoit-Bird & Au, 2009). In the late 1970’s photographic identification techniques

were developed allowing for individual identification of animals from the unique

markings on the trailing edge of their dorsal fins (Würsig & Würsig, 1977). This has

allowed researchers to study many aspects of their biology such as association patterns,

10

group size, residency patterns, distribution and habitat use (Würsig & Jefferson, 1990). As

a result, much has been discovered about their ecology and behaviour in the last thirty

years.

Bottlenose dolphins are in the Delphinidae family in the suborder Odontoceti. Their

systematics remains unresolved due to major variations in morphology, colouration,

physiology and genetic structure with geographic location (LeDuc et al., 1999; Natoli et

al., 2004; Wells & Scott, 1999). Based off both morphological and genetic evidence, it is

generally accepted that there are three species within the genus (Charleton-Robb et al.,

2011; Goodall et al., 2011; Kurihara & Oda, 2001; Perrin et al., 2007). The common

bottlenose dolphin (T. truncatus), has the widest distribution from the cold temperate

through to the tropics and is characterised by inshore and offshore ecotypes within

regions that differ in factors such as gross morphology, haematology and parasitic load

(Connor et al., 2000). The distribution of the Indo-Pacific bottlenose dolphin (T. aduncus)

is limited between the warm temperate and tropical Indo-Pacific. Lastly, the newly

discovered Burrunan dolphin (T. australis) (Charlton-Robb et al., 2011) is found only in

south/south-eastern Australia.

1.6.1 Ecology

Bottlenose dolphins are a cosmopolitan species that are distributed from the tropics

through to the cold temperate (approximately 60ᵒN to 55ᵒS), encompassing all major

oceans, and are common in both pelagic and coastal waters (Goodall et al., 2011; Jefferson

et al., 2008; Olavarría et la., 2010; Wilson et al., 1997). Offshore populations have been

studied comparatively less than coastal populations; however they are found around

oceanic islands and in open waters (Scott & Chivers, 1990; Silva et al., 2008). Within

coastal waters, bottlenose dolphins exploit a range of habitats including bays e.g. Sarasota

Bay, Florida (Ballance, 1990), estuaries e.g. Charleston County, South Carolina (Zolman,

2002), tidal inlets e.g. Moray Firth, Scotland (Wilson et al., 1997), and mangroves e.g.

Peru (Van Waerebeek et al., 1990). Their success in exploiting such a diversity of

environments has been attributed to their behavioural plasticity, which has enabled them

to develop specialised location-dependent foraging strategies (Connor et al, 2000; Shane,

1990) e.g. strand-feeding (Duffy-Echevarria et al., 2008; Sargeant et al., 2005), mud-

plume feeding (Lewis & Schroeder, 2003). Some populations have even learned how to

exploit human activities to obtain such as following trawling boats to retrieve thrown

away fish (Chilvers & Corkeron, 2001).

11

1.6.2 Social structure

Bottlenose dolphins live in fission-fusion societies where individuals live in groups that

change in composition on a regular basis (i.e. hourly, daily) (Connor et al., 2000; Würsig,

1978). This type of social system is characteristic of a number of species including

elephants, (Couzin, 2006), bats e.g. Myotis bechsteinii (Kerth et al., 2006), spotted hyenas

Crocuta crocuta (Smith et al., 2007), and a number of primates e.g. chimpanzees Pan

troglodytes versus (Lehmann & Boesch, 2004), spider monkeys Ateles chamek (Wallace,

2008). It has been suggested that by adopting a fission-fusion social structure, groups

within populations are able to adjust their composition in response to spatio-temporal

fluctuations in ecological pressures e.g. food availability, predation risk (Lehmann et al.,

2007; Pearson, 2009). While all bottlenose dolphins live in fission-fusion societies, inter-

population variation in variables such as group size, residency patterns and association

patterns exist suggesting that populations are locally adapted to deal with specific

challenges associated with their environment.

1.6.3 Association patterns:

While composition is dynamic, associations are not random, and for a number of

populations, some broad generalisations can be made concerning age- and sex-specific

patterns (Shane et al., 1986). The most consistent and strong relationship between

individuals is that seen for mothers and calves, which is a result of a long nursing period

of around 3-8 years (Gibson & Mann 2008; Quintana-Rizzo & Wells, 2001; Scott et al.,

1990). Adult and sub-adult females can either be solitary or exist in groups of varying

size. They often form associations with other females of a similar reproductive state

(Möller & Harcourt, 2008) and for some populations (e.g. Shark Bay, Western Australia)

there is evidence that kinship may play an important role in determining relationships

(Frere et al., 2010). In some populations (e.g. Sarasota Bay, Florida and Shark Bay,

Western Australia) males form alliances to coerce females into courtships (Connor et al,

1992; Connor et al., 2001; Scott et al., 1990; Wells, 1991). Therefore the potential

reproductive benefits males can obtain by forming these alliances appear to explain

association patterns (Wiszniewski et al., 2012). Mixed-sex groups also occur which are

explained as mating aggregations (Eisfeld & Robinson, 2004). However, unique

ecological challenges specific to a geographic location also affect association patterns,

resulting in mixed-sex groups irrespective of mating goals. For example, in Doubtful

Sound long-lasting associations of up to seven years occur without preference to sex,

which is unusual for this species. They concluded that due to their geographical isolation

12

and the unpredictable nature of the fiord, the formation of long-lasting associations

between and within sexes were important for information transfer (Lusseau et al., 2003).

Therefore it appears that it is a combination of socio-ecological factors that influence

association patterns, which are population specific (Gero et al., 2005).

1.6.4 Residency patterns

The way in which animals use their environment is dictated by both environmental

variables such as habitat structure and geographic isolation, as well as biological traits

such as philopatry (Bearzi et al., 2008). Residency patterns are highly variable between

bottlenose dolphin populations. Some exhibit high levels of site fidelity forming semi-

closed populations e.g. Amvrakikos Gulf, Greece (Bearzi et al., 2008). In other locations,

many individuals may exhibit high levels of site fidelity; however, they represent only a

fraction of a larger population e.g. the Marlborough Sounds, New Zealand (Merriman et

al., 2009). At the other end of the spectrum, others exist as open populations in which few

individuals display site fidelity e.g. Kino Bay, California (Ballance, 1990)

1.6.5 Group size

Mean group size for bottlenose dolphins is highly variable (between 5 to 140 individuals),

which is in part a result of the use of different definitions of what constitutes a group

(Connor et al., 2000). In general, group size tends to increase with water depth and

openness of habitat (Shane et al., 1986), which has been accredited to an increase in

predation risk and changes in prey distribution (Connor et al., 2000). In shallower waters

living in a small group is optimal as it decreases competition between individuals. In

contrast, larger groups are favourable in deeper waters where prey distribution is patchy

and therefore cooperative searching and information transfer is beneficial (Benoit-Bird &

Au, 2003). It has also been suggested that large group sizes offer better protection from

predators in open water through increased predator detection (Campbell et al., 2002);

however, this is not always the case and depends on the ecology of the local predators

(Heithaus & Dill, 2002). Groups containing calves are generally larger (Campbell et al.,

2002; Rogers et al., 2004) and it is suggested that increased protection of young is the

driver of this. However, Mann et al. (2000) found that group size was not a good predictor

of calf survival suggesting that other factors might be at play. Group activity can also have

an effect on group size, with socialising groups being of larger size (Bräger et al., 1994;

Shane et al., 1986).

13

1.6.6 Bottlenose dolphins in New Zealand

Genetic analysis has confirmed that the species of bottlenose dolphins inhabiting New

Zealand’s waters is the common bottlenose dolphin (Tezanos-Pinto et al., 2009). It is

distributed along the coastline in a discontinuous manner with three main populations

recognised in Northland, Marlborough Sounds, and Fiordland (Bräger & Schneider, 1998;

Constantine, 2002; Currey, 2008; Merriman et al., 2009). The Fiordland population is

further subdivided into three communities in Doubtful Sound, Dusky Sound, and Milford

Sound (Currey, 2008). Comparisons of photo-identification catalogues between the three

main sub-populations suggest a high degree of isolation, which has been confirmed from

genetic analysis using haplotype diversity (Tezanos-Pinto et al., 2009). Population

estimates have been generated for each of these areas using mark-recapture models. The

Northland population was estimated to include 483 (95% CI 358-653) individuals

(Tezanos-Pinto et al., 2013); 211 (95% CI 195-232) individuals in Marlborough Sounds

(Merriman et al., 2009); and 205 (95% CI 192-219) individuals in the Fiordland

population (Currey, 2008).

While bottlenose dolphins are in the Least Concerned Category of the IUCN Red List as

they are widespread and globally abundant (Hammond et al., 2012), many local

populations around the world are in decline as a result of human activity. Indeed, this is

the case for two of the three subpopulations in New Zealand. In Doubful Sound,

population decline has been attributed to low survival rates of calves (0.38, 95% CI, 0.21-

0.58) due to disturbances associated with tourism, boat activity and hydroelectric

generation (Currey et al., 2009b). As a result, the IUCN has upgraded the Fiordland

population to Critically Endangered due to its small size (Currey et al., 2009a).

The second population in decline is Northland, of which the Bay of Islands is the primary

habitat of the population; the focus of this thesis. The Bay of Islands has been identified

as a critical part of the population’s range where individuals are sighted all year-round

(Constantine & Baker, 1997; Constantine, 2002; Tezanos-Pinto, 2009; Hartel, 2010). As a

result, most of the information available on the Northland population comes from the Bay

of Islands. Here, dolphins have been studied since 1993, providing a long-term data set to

assess many aspects of their ecology and biology (Constantine & Baker, 1997;

Constantine, 2002; Mourão, 2006; Tezanos-Pinto, 2009; Hartel, 2010).

The Bay of Islands is a popular holiday spot with a high level of recreational and

commercial boat activity, especially in the summer months. Commercial dolphin-based

tour operators run on a daily basis within the bay from which people watch and swim-with

14

the dolphins. Research by Constantine (2001; Constantine et al., 2004) showed that these

interactions affect the dolphin’s behaviour, interrupting important activities such as rest,

and essentially displacing them from daily activities. The population has undergone a

number of changes since research was first initiated. There has been a considerable change

in habitat use, in the individuals that use the bay regularly (Hartel, 2010), and a marked

decline in abundance (7.5% annual decline) (Tezanos-Pinto et al., 2013). It was concluded

that high calf mortality, mortality of individuals that previously used the bay frequently

and a change in ranging behaviour may explain the decline. However, a preliminary

examination of potential casual factors for the decline was conducted, but found no clear

changes in environmental variables (Nathan, 2010). Photo-identification work in the

Hauraki Gulf revealed that 65% of the catalogued individuals in the Hauraki Gulf (n=162)

were catalogued in the Bay of Islands, suggesting a wider ranging population than first

thought, or that overlap of home ranges between individuals with differing core areas

(Tezanos-Pinto, 2011). The social structure of dolphins in the Bay of Islands was last

investigated by Mourão (2006), suggesting a fission-fusion society characterised by short

term acquaintances and long term companionships within and between both sexes.

Bottlenose dolphins are highly social mammals and the loss of individuals can lead to

social disruption, which may have serious implications for the health of the population

(Augusto et al., 2012). As a result, it is crucial that this population is continued to be

monitored and important aspects of their ecology and biology investigated in order to

inform conservation managers.

1.7 Thesis aims and objectives

This thesis adds to a 19- year research project on bottlenose dolphins the Bay of Islands,

New Zealand. The first aim of the study was to assess the current status of the population

by providing up-to-date estimates of abundance and survival. The second aim of the study

was to investigate the social structure of the population.

More specifically, the objectives of this research were to:

1. Estimate abundance, survival and temporary emigration rates of bottlenose

dolphins in the Bay of Islands using mark-recapture techniques covering the time

period 2009, using data collected by Hartel (2010), to 2012 (this research) (Chapter

2);

15

2. Describe group composition, strength of associations between individuals, and

their temporal stability for bottlenose dolphins in the Bay of Islands, and to assess

whether there are sex-specific association patterns (Chapter 3)

The population of bottlenose dolphins in the Bay of Islands has undergone a number of

significant changes in the past 19 years. The area experiences a high volume of both

commercial and private boat traffic of which bottlenose dolphins are sensitive to

(Constantine, 2001; Constantine et al., 2004; Hastie et al., 2003; Lusseau, 2005). The last

estimates of abundance were for the period 1997-2006 and a significant decline of 7.5%

per annum was detected. As a result, it is critical that new estimates of abundance and

survival are obtained. Moreover, for bottlenose dolphins, where strong bonds exist

between individuals, the loss of individuals may cause social disruption and therefore

gaining insight into the association patterns is essential (Augusto et al., 2012; Sutherland,

1998; Whitehead, 2008a) By conducting this study, I hope to provide current information

concerning the population status and social structure of bottlenose dolphins in the Bay of

Islands that can inform management decisions.

Chapter 1 Introduction

Chapter 1 gives a literature review on abundance and population dynamics, habitat use,

and sociality of animals and explores the socio-ecological factors that shape them. It also

gives a general introduction to Tursiops spp.

Chapter 2 Abundance, Survival, and Temporary Emigration

Chapter two provides a current estimate of abundance and survival rates while taking

temporary emigration into account.

Chapter 3 Social Structure

This chapter describes the social structure of bottlenose dolphins in the Bay of Islands.

More specifically, it looks at the strength of associations between individuals, whether or

not there are sex-specific associations, and investigates the temporal stability of them.

16

2 Abundance, Survival and Temporary Emigration

2.1 Introduction

A primary goal in population biology is to assess the abundance of a population, which is

important for both theoretical and applied reasons (Pollock et al., 1990). However,

abundance is not static, but rather fluctuates in time as a result of changes in four

population parameters: birth, death, immigration and emigration (Anderson, 1974). It is

therefore equally important to explain trends in population size in order to describe the

population dynamics.

2.1.1 Mark-recapture methods

Mark-recapture methods (MR) have been widely used by researchers as a tool for

estimating population parameters such as survival, recruitment, mortality and abundance

(Chao, 1987; Otis et al., 1978; Pollock et al., 1990). A fundamental criterion of mark-

recapture is that animals are individually recognisable through time. Earlier MR studies

relied on artificial markings such as radio-tags (Pollock et al., 1989) and bands (Karr et al.,

1990). However, in recent times it has become apparent that for a number of large, long-

lived vertebrates, individuals can be recognised by natural markings e.g. zebra Equus

burchelli (Petersen, 1972), black rhinoceroses Diceros bicornis (Goddard, 1966) and the

African elephant Loxodonta africana (Morley & van Aarde, 2007). MR studies involve

two or more sampling occasions. In the first sampling occasion, a portion of the

population is caught, marked, and released back into the wild. On each subsequent

sampling occasion, new unmarked animals are marked, previously marked individuals

have their capture recorded, and all individuals are then released. At the end of the study

period the researcher has a comprehensive record of the capture history of each animal

that was sampled. Criteria concerning mark quality are stringent for MR studies involving

population parameter estimates. It is critical that these marks are recognisable over time

(i.e. long lasting or permanent), be unique to the individual, and are of the same quality

between individuals (Würsig & Jefferson, 1990).

MR methods were first applied to cetaceans when photo-identification techniques were

developed in the late 1970s (Connor et al., 2000). Photo-identification techniques allow

for individuals within a population to be recognised by their unique markings and are

widely used in cetacean studies (Hammond et al., 1990). This has led to great

advancements in our understanding of population biology and ecology. However,

17

misidentification of individuals has serious consequences for estimates of population

parameters. There are two ways in which false identification in subsequent surveys can

create bias in estimates (Stevick et al., 2001). The first is falsely identifying one individual

as two (false negative error). Conversely, two individuals can be mistaken as one (false

positive error). Both of these errors may arise from using poor quality photos in the

analysis, including individuals with undistinctive markings, sampling in poor weather

conditions, or as a result of changes in markings over time (Friday et al., 2000; Gowans &

Whitehead, 2001). As a result, quality control criteria for photographs are vital in MR

studies using photo-identification techniques.

2.1.2 MR models

A number of MR models and methods have been developed. These are probability models

that use the method of maximum likelihood estimation and therefore provide a statistically

robust way to assess population parameters (Manly et al., 2005). MR models are based on

a set of assumptions that relate to both the nature of the population, as well as sampling

design (Otis et al., 1978; Read et al., 2003). They depend on the validity of these

assumptions and any violations of them can lead to bias and/or poor precision of the

estimates (Seber, 1973). MR models have traditionally been split into two categories;

closed and open models. While there are some general assumptions that must be held for

both sets, others are only applicable to each of the categories, meaning that they are

appropriate for different applications (Pollock et al., 1990).

There are four assumptions that must be met when using closed MR models: 1) the

population is closed to birth, death, emigration and immigration; 2) animals do not lose

their marks; 3) all marks are correctly noted and recorded at each trapping occasion and;

4) each animal has a constant and equal probability of capture on each trapping occasion.

The first assumption refers to both demographic and geographic closure and is the primary

assumption that separates closed models from open (Kendall et al., 1995). Due to this

assumption, sampling periods are usually limited to a short time frame (Otis et al., 1978;

Pollock, 1991). Because the population is essentially assumed static, closed MR models

are used to derive estimates of population size. However, the assumption of closure

restricts the amount of information that can be obtained, and there closed models cannot

be used to detect trends in the parameters that affect population size such as survival and

emigration (Otis et al., 1978).

The assumption of equal capture probabilities is rarely met in studies of wild animal

populations and violations of this assumption can cause serious bias in estimates of

18

abundance. There are three ways in which this assumption can be violated (Pollock, 1991).

The first results from variation in capture probabilities with time: this often arises from

changes in environmental conditions within the site over the study period. The second is a

behavioural response to the initial trapping event; here the animal may become trap happy

or trap shy. The third source of unequal capture probabilities may be due to inherent

differences in biological and behavioural traits (heterogeneity). Examples of this include

differences in age, sex and social dominance and home ranges relative to trapping area

(survey site). Heterogeneity in capture probabilities can also result from photo-

identification methods. Pollock (1974) considered eight models allowing for unequal

capture probability that were then later developed by Otis et al. (1978). The ability to

model heterogeneity, time and behavioural response into capture probability has relaxed

the assumption of equal capture probability, decreasing bias in population estimates.

In a number of cases, it is unrealistic to assume that the population under study is

demographically and geographically closed (Pollock, 1991). As a result, open MR models

are often applied. Assumptions associated with open models are: 1) every animal present

in the population at a particular sampling occasion has the same probability of capture, 2)

every marked animal in the population has the same probability of survival between

sampling periods, 3) marks are not lost or overlooked, 4) all samples are instantaneous and

animals are released immediately after capture and 5) all emigration is permanent. One of

the original open MR models was the Jolly-Seber model, which estimates apparent

survival rates (i.e true survival rate and complement of permanent emigration), capture

rates, population size and numbers of new animals (Jolly, 1965; Seber, 1965). However,

the assumption of equal catchability is often violated due to factors such as individual

heterogeneity.

Estimates of abundance are sensitive to heterogeneity in capture probability, often leading

to bias, and therefore the use of open models for this application are generally

unfavourable (Pollock et al., 1990; Sandercock, 2006). Open MR models offer a reliable

way to obtain survival estimates as they are reasonably robust against heterogeneity in

capture probabilities (Carothers, 1973) and are unaffected by behavioural response

(Nichols et al., 1984). The Cormack-Jolly-Seber (CJS) model is a restricted version of the

JS model, which is used to obtain apparent survival estimates, and has been widely

applied to a range of taxa since its development (Freilich et al., 2000; Lindenmayer et al.,

1998; Morrison et al., 2004). The assumption of permanent emigration is rarely met, and

violations to it create bias in estimates of a number of parameters including both

19

population size and survival rates when using standard open MR models (Pollock et al.,

1990; Seber, 1982).

Pollock (1982) first proposed a sampling design that combined both open and closed MR

models called the Robust Design (RD). The RD consists of two levels of sampling that

operate over different time scales: primary sessions and secondary samples (Nichols,

2005). Secondary samples are taken consecutively in a short time interval and collectively

make up a primary session. This time interval is short enough in time for the assumptions

of demographic and geographic closure to be met. Primary sessions essentially represent

closed models; data from the secondary samples are used to estimate abundance. In

contrast, the time between primary sessions is long enough in time for losses (death,

emigration) and gains (birth, immigration) to the population to occur. The initial design

has been developed into a full set of multinomial statistical models that use the full

likelihood approach (Kendall, 2001). They allow for variation in capture probabilities due

to time, heterogeneity and behavioural response, which increase the precision in survival

estimates (Kendall et al., 1995). The assumption of permanent emigration for classic open

MR models has been relaxed by allowing temporary emigration to occur (Kendall &

Nichols, 1995; Kendall et al., 1997). Temporary emigration occurs when members of the

population are not always available for capture (Silva et al., 2009). Overall, the RD

capitalises on the strengths of both open and closed models in estimating certain

population parameters (open models: survival rates; closed models: abundance), which

reduces bias in parameter estimates and allows for better precision of a greater number of

parameters to be estimated at any one time.

The importance of obtaining estimates of temporary emigration in mark-recapture studies

has been recognised for a range of taxa, including a number of birds (Hestbeck et al.,

1991; Nichols & Kaiser, 1999), Plethodon salamanders (Bailey et al., 2004), and the

alpine newt Triturus alpestris (Perret et al., 2003). In the case of cetaceans, attention to

temporary emigration has mostly been given to migratory species such as western gray

whales Eschrichtius robustus (Bradford et al., 2006) and blue whales Balenoptera

musculus (Ramp et al., 2006). For delphinids, the study site may only represent a portion

of the population’s range and therefore not all individuals will be consistently available for

capture. In these cases, models that incorporate temporary emigration not only lead to

better precision in estimates, but also provide biologically interesting information

regarding dolphin movements (e.g. Cantor et al., 2012; Nicholson et al., 2012; Silva et al.,

2009; Tezanos-Pinto et al., 2013).

20

2.1.3 Study population

In New Zealand, three geographically discontinuous populations are recognised in

Northland, Marlborough Sounds, and Fiordland. These three populations show

differentiation in mitochondrial DNA haplotype frequencies, indicating that there is little

exchange of individuals between them (Tezanos-Pinto et al., 2009). The Northland

bottlenose dolphin population is widely distributed, mainly ranging along the east coast

between Doubtless Bay and Tauranga (Constantine, 2002). They are occasionally sighted

further in the Manukau Harbour on the west coast of the North Island (Constantine, unpub

data). The Bay of Islands forms a critical portion of their habitat where individuals are

found all year round (Constantine, 2002). For this reason the majority of information

available on this population comes from studies within the bay (Constantine 1995;

Constantine, 2002; Hartel, 2010; Mourão 2006; Ryding, 2001; Tezanos-Pinto, 2009). A

photo-identification catalogue has been compiled since research was first initiated in 1993,

with 496 uniquely marked individuals sighted within the region at least once.

For the Bay of Islands, the application of the RD to obtain estimates of population

parameters is favourable for both biological and practical reasons. While the Northland

population is geographically isolated from other coastal populations in New Zealand

(Tezanos-Pinto et al., 2009), the Bay of Islands represents a small portion of the entire

range, with varying degrees of movement among individuals (i.e. core users, occasional

visitors and transients (Berghan et al., 2008; Constantine, 2002;Tezanos-Pinto, 2009).

There is generally only one group within the bay at any one time; however, groups are

rarely stable for more than a few days (chapter 3), or move out of the bay completely to be

replaced by a new group (Mourão, 2006). Over short time periods groups are usually

stable enough so that the population is essentially closed, allowing for estimates of

abundance to be derived within these time frames. The dynamic nature of the population

over the longer term fits open MR methods, allowing for survival and temporary

emigration to be estimated between sessions. By using the RD, a greater number of

population parameters can be estimated at one time.

The RD was recently used to estimate abundance, survival and temporary emigration from

1997-2006 (Tezanos-Pinto et al., 2013). There was clear evidence for temporary

emigration of the population from the Bay of Islands, and while survival rates were

comparable with estimates reported for other populations e.g. western Gulf of Shark Bay,

Western Australia (0.95, 0.87-0.98, Nicholson et al., 2012), Doubtful Sound, New Zealand

(0.93, 95% CI 0.917-0.953, Currey et al., 2009b), abundance estimates showed a 7.5%

21

annual decline (Tezanos-Pinto et al., 2013). The decline in abundance is of concern,

especially as the population remains under a considerable amount of pressure from

anthropogenic activities, primarily from an intensive dolphin swim/watch tourism industry

(Constantine, 2001; Constantine et al., 2004). Moreover, it is a busy holiday area where a

large number of private boats frequent the waters. A number of studies have shown that

both dolphin tourism, as well as general boat traffic, induce short term behavioural

responses (Constantine et al., 2004; Hastie et al., 2003; Mattson et al., 2005; Nowacek et

al., 2001), and the accumulative effect of this may have serious implications for the

population (Bejder et al., 2006a; 2006b). As a result, it is important to continue to monitor

this population to guide management decisions. The objective of this chapter is to use the

RD to obtain estimates of abundance and apparent survival rates for bottlenose dolphins in

the Bay of Islands.

2.2 Methods

2.2.1 Study site

The study site was situated on the east coast of northern New Zealand in the Bay of

Islands (35°15’S, 174°15’E) (Fig. 2.1). It is a large, convoluted embayment with an area

of approximately 260km2. Ninepin (Tikitiki) and Piercy (Motukokako) Island represent

the 15km mouth of the bay. It encompasses 144 islands which are mainly located in both

the western and the southeastern parts of the bay. The bay contains a variety of habitat

types including mangroves and saltwater marshes within estuaries, rocky coasts and sandy

beaches. Water depth ranges from a maximum of 65m in the outer bay to a shallower

average of 12m in the inner bay (Booth, 1974). There are three major inlets: Waikare,

Kerikeri and Te Puna, and four main rivers: Kerikeri, Waitangi, Kawakawa and Waikere.

Collectively, these provide the majority of the freshwater entering the bay. The area is

characterised by a sub-tropical oceanic climate with sea surface temperature (SST) ranges

from 13.5°C in winter to 22.5°C in summer.

22

Figure 2.1 The Bay of Islands. The study was conducted within the harbour boundary, which lies

between Ninepin and Piercy Islands. Green represents land and dark blue indicates deeper water

(Hartel, 2010).

2.2.2 Boat surveys

Boat surveys were conducted to photo-identify individuals between March 2009 – January

2010 (Hartel, 2010) and February – December 2012 within the Bay of Islands, Northland

following methods by Würsig & Jefferson (1990) and Bearzi et al. (1997). Surveys were

conducted from a 5.1 metre independent research vessel within daylight hours in a

Beaufort Sea state of four or less. Due to the convoluted nature of the bay, and the fact that

one group is typically found within the bay at any one time (Constantine, 2002; Hartel,

2010), surveys were not conducted along a predetermined route. Instead, areas where the

dolphins are commonly found were searched first, and information on the dolphin’s

whereabouts was obtained from dolphin tour boat operators who travelled widely

throughout the bay on their trips. The boat was driven between 10 and 20 knots during

search time and a 360⁰ area around the boat was visually scanned until the dolphins were

sighted.

23

A group was defined as any group of dolphins in apparent association, moving in the same

direction and often, but not always, engaged in the same activity (Shane, 1990). When

approaching the group, boat speed was reduced and the boat was driven parallel to the

group at a speed that matched theirs to minimise effects of the boat (Constantine et al.,

2004).

Photographs were taken of the unique markings along the trailing edge of the dorsal fin of

individuals using a D40 Canon DSLR with a 100-300mm lens. Individual dolphins were

photographed at random, as many times as possible, irrespective of the degree of their

markings to reduce bias towards particularly recognisable individuals (Cantor et al., 2012).

Surveys ended either when we were confident that we had successfully photographed

every member of the focal group, when weather conditions deteriorated to a state at which

the survey had to be aborted, or when the dolphins were lost.

2.2.3 Photographic data analysis

Photographic control is necessary to reduce heterogeneity in capture probabilities created

through misidentification rates (Cantor et al., 2012; Gowans & Whitehead, 2006).

Misidentification is likely to occur when the quality of the photo is low and/or individuals

have small indistinguishable marks that contain very little information (Gowans &

Whitehead 2006; Stevick et al., 2001). All photographs from both 2009 and 2012 were

analysed and separated into four categories based on the photographic quality. The quality

of each photograph was determined by the sharpness, angle, brightness and contrast, and

size of the fin relative to the frame (table 2.1). From good (scale 3) and excellent (scale 4)

quality photographs, individuals were given a score based on how much information their

nick patterns provided. Nick distinctiveness was categorised based on a system developed

by Urian et al (1999) (table 2.2). Individuals with very distinct fins that could be

recognised by their nicks in poor quality photographs were given a score of D1. Those

whose nicks provided an average amount of information were given a score of D2 (one

larger, distinctive nick or several smaller nicks). Individuals were given a score of D3

where little information from their markings could be obtained (scarring, small

indistinguishable nicks). While other markings such as tooth rakes and pigmentation can

also be used to identify individuals, they were used secondary to markings on the dorsal

fin as they are not permanent (Williams et al. 1993; Würsig & Jefferson, 1990). Only

individuals that had D1 and D2 ratings from good and excellent photographs were used in

the analysis. For dolphins, marks are accumulated with age and therefore the degree of

24

nick distinctiveness is usually indicative of their age class. As a result, the analysis was

mostly restricted to the adult population.

The best photograph of each individual from the survey was compared to the photos from

the Bay of Islands catalogue to see if the individual had been previously sighted in the

bay. This catalogue has been curated since 1993 and contains the best quality photograph

obtained of each individual sighted at least once. If a dolphin could not be matched, at

least one other researcher attempted to match it. If they were unsuccessful at doing so the

individual was assigned a number and added to the catalogue.

25

Table 2.1 Scale of photo quality and attributes used to evaluate the photo quality of sighting data for

bottlenose dolphins in the Bay of Islands. Adapted from Tezanos-Pinto (2009).

Scale Rank Attributes Examples

1 Poor photographs

Three or more attributes failed to comply (brightness, contrast, focus, angle and/or size), or one or more attribute significantly impaired nick visualisation. Information content is severely compromised by poor photographic quality

2 Fair photographs

Two attributes failed to comply; however information content is not compromised by photographic quality

3 Good photographs

One attribute failed to comply. Information content is retained

4 Excellent photographs

All attributes complied

26

Table 2.2 Scale of nick distinctiveness based on a system devised by Urian et al (1999). D1 represents

individuals with very distinctiveness markings, D2 with moderate markings and D3 with markings

that contain little information.

Distinctiveness Description Example

D1

Very distinctive notch pattern on the dorsal fin that can be recognised from poor quality photographs

D2

One large notch or several small ones. Average amount of information available from markings

D3

One small indistinguishable nick or scarring only. Markings contain very little information

2.2.4 Data organisation

Capture histories were created for each photo-identified individual that met the analysis

criteria. A capture history is a record of the sighting history for each individual captured

during the study period. For each sampling day, the presence of an individual was denoted

by a 1 and its absence by a 0. In 2012, data was specifically collected to fit the RD design;

four field trips (primary sessions) of up to four consecutive days of surveys (secondary

samples) were taken. The 2009 data set was not specifically collected for the RD analysis

as photo-identification was part of a longer project on the dolphin’s habitat use (Hartel,

2010). However, surveys were conducted in near consecutive days in most months,

allowing for subsets of the data to be extracted to fulfil the design requirements of the RD.

Five primary sessions were selected between June and December that were composed of

secondary samples of 3-5 survey days. The intervals between primary sessions were

specified in decimal years between their mid dates to obtain annual estimates of apparent

27

survival. Mark-recapture models were built using the capture histories using program

MARK (White & Burnham, 1999).

2.3 Statistical analysis

2.3.1 Model assumptions

For the most part, the assumptions of the RD are a combination of those for the closed-

population models and Cormack-Jolly-Seber (CJS) open-population models (see section

2.1.2). Additional assumptions specific to the RD are (a) temporary emigration between

primary sessions is either Markovian or random (b) survival probabilities between the

midpoint of primary session and is the same for all animals in the population,

regardless if they are available for capture (i.e. within or off the study site).

Assumptions were validated using goodness of fit tests (GoF) and from information

available on the biology of the population. Goodness of fit tests provides a statistical tool

for testing assumptions underlying the fitted models. No GoF tests have been developed

specifically for the RD. However, because the RD is a combination of the CJS open

models and closed models it is possible to use traditional GoF tests from each of these to

check for violations.

To validate the assumptions that all marks are unique, are not lost over the duration of the

study period, and all individuals are correctly identified upon recapture, photo-quality

control was employed (see section 2.2.3). Nick distinctiveness was evaluated for each

individual seen during the study and only those with unique markings that could be

unequivocally identified from good or excellent quality photographs were included. The

Bay of Islands fin catalogue has been regularly updated since 1994 and therefore new

marks on fins are generally detected. If there was any uncertainty when matching an

individual, another researcher was asked for a second opinion to minimise bias that arises

misidentification (i.e. false negatives and false positives).

It is difficult to evaluate the assumption of independence of capture between individuals.

Bottlenose dolphins are live in complex societies, where individual’s form preferred

and/or avoided associations with one another (see chapter 3). However, this alone is

unlikely to cause bias, but rather lead to slightly underestimate of the standard error

(Williams et al., 2002).

Primary sessions were short (3-5 days) compared to the time between them, which

validates the assumption that sampling is instantaneous relative to the time between

sampling periods. Additionally, the process of photo-identification does not remove

28

individuals from the population and therefore it is reasonable to assume that individuals

are released immediately.

The program CloseTest (Stanley & Burnham, 1999) was used to assess whether the

assumption of closure was violated for each of the primary sessions. The program

incorporates two tests; those designed by Stanley & Burnham (1999), and those by Otis et

al. (1978). While the latter is robust against heterogeneity in capture probabilities, it is

sensitive to time or behavioural variation in capture probabilities and insensitive to

temporary violation of closure in the middle of the survey (Otis et al., 1978; Stanley &

Burnham 1999). In contrast, the test produced by Stanley & Burnham (1999) allows for

time variation in capture probabilities, but is highly sensitive to heterogeneity and

behavioural differences in capture probability. Therefore these tests complement each

other and were both used to assess closure.

The assumption of homogeneity in capture probability has been somewhat relaxed by the

incorporation of models that allow for heterogeneity, behavioural response and time

variation in capture probabilities into MR models (see introduction). Each of the closed

sessions were analysed in Program CAPTURE (as implemented in MARK; White &

Burnham, 1999) to test for the potential effects each of these sources in capture

probability.

To test the assumptions associated equal capture and survival probabilities between

primary sessions, capture histories were pooled by session (i.e. were either sighted at least

once during a primary session, or not sighted) and U-CARE V 2.3.2 (Choquet et al., 2005)

was used to run GoF tests (TEST 2 and TEST 3). Both TEST 2 and TEST 3 are

themselves partitioned. Overall, TEST 2 assesses whether the assumption of equal

catchability has been violated. TEST2.CT tests the null hypothesis that there is no

difference in the probability of being recaptured at i+1 between those captured and not

captured at time i, conditional on the presence at both occasions. Here a specific test

statistic incorporated to assess for a behavioural response to capture, and the direction of it

(i.e. trap happy z<0; trap shy z>0). TEST2.CL tests the null hypothesis that there is no

difference in the expected time of recapture between individuals captured and not captured

at occasion , given they were both present at occasions and i+2. While this test has no

simple interpretation, it can indicate whether there is a lasting trap response (i.e. more than

one sampling occasion). TEST 3 looks for violation of the assumption that all marked

animals have the same probability of surviving between sampling occasions. TEST3.SR

tests whether there is a difference in the probability among previously and newly marked

29

individuals captured at time i of being recaptured at some later time. This test specifically

incorporates a test statistic for a transience effect (i.e. dolphins sighted only once during

the course of the study; z>0.05). TEST3.SM examines whether there is an effect of

capture on survival by testing the hypothesis that there is no difference in the expected

time of first recapture between old and newly marked individuals captured at occasion i

and seen at least once again. The highly social nature of dolphins means that sightings

between individuals are not independent, which creates over-dispersion (extra-binomial

noise) in the data (Anderson et al., 1994). Over-dispersion was tested for by deriving the

variation inflation factor ( ), which was estimated from the bootstrapping approach

available in MARK, as well as from the global test statistic/df produced in U-CARE. If

over-dispersion was detected, the conservative approach was taken by using the by

selecting the highest estimate of to adjust for the lack of fit in the RD models (Silva et

al., 2009; White & Burnham, 1999)

2.3.2 Closed Robust Design

A set of RD models were then developed, which are composed of the following

parameters: apparent survival , which is the probability that an individual survives from

primary period to period and stays in the area; the probability of capture ; the

probability of temporary emigration given it was alive and present within the study site in

the previous primary session ( ’’) and; the probability of that an emigrant will remain

outside of the study area given it was absent in the prior primary session ( ’). For each

primary session, abundance for dolphins in the bay and capture probability were

estimated. Apparent survival and temporary emigration were estimated from the intervals

between primary sessions (Kendall & Nichols, 1995; Kendall et al., 1997). Recapture

probabilities were constrained to equal capture probabilities as the evidence for a

behavioural effect was minimal (table 2.4).

Three different temporary emigration models were considered, (1) no temporary

emigration . This represents the null hypothesis where there is no temporary

emigration at all; (2) random emigration where the probability of an individual

being available in the i+1 primary session is independent of its state in primary session i

and (3) Markovian , where the probability of an individual being available for

capture in a primary session is conditional on its availability in the i primary

session. For each of these temporary emigration patterns, models were considered where

survival either varied with time or were kept constant . Models were considered

30

with time dependence in temporary emigration parameters as dolphins exhibit seasonal

movements within the bay (Hartel, 2010).

RD models were first built with full time dependence in capture probability as it is

likely that environmental conditions were not stable throughout the duration of this study

and there was evidence for time variance of capture probability within sessions. However,

because primary sessions were relatively short (3-5 days), models were also considered

with constant capture probability within primary sessions, varying only between sessions.

For Markovian models where there was time dependence in apparent survival probabilities

, constraints were placed on γ’’, γ’ to provide parameter identifability. This was achieved

by setting the last two time periods equal to each other

. In

the case of no movement models, temporary emigration parameters were fixed.

The Quasi-likelihood Akaike’s Information Criterion QAICc was used to compare models

and assess the relative support for each of them. The QAICc provides an effective way to

deal with over-dispersion when comparing models (Anderson et al., 1994; Seber, 1992),

and was therefore used in place of the standard Akaike’s Information Criterion. The model

with the lowest QAICc was chosen as it represents the most parsimonious model (Johnson

& Omland, 2004). However, models within two QAICc units should not be rejected they

still have considerable support from the data (Burnham & Anderson, 2002).

2.3.3 Mark rate

As with other populations of bottlenose dolphins, not all individuals are identifiable as

they do not have sufficiently distinctive markings (Würsig & Jefferson, 1990). As a result,

abundance estimates from MR models only pertain to the proportion of marked

individuals within the population. To account for unmarked individuals, the mark ratio

was calculated (Jolly, 1965), which is the proportion of distinctly marked individuals in

the population. For all photographs that met the criteria (table 2.1 and 2.2), the proportion

of identifiable individuals was estimated by calculating the ratio of photographs

containing D1 and D2 graded individuals by the total number (i.e. marked and unmarked)

of photographed fins (Williams et al., 1993). If fins have been photographed at random

without preference to nick distinctiveness, then the ratio should reflect the true proportion

(Gormley et al., 2005).

An alternative way to estimate the proportion of identifiable individuals is to divide the

number of individuals with recognisable fins by the total number seen within a survey.

The latter is only appropriate when group sizes are small and the characteristics of the

31

study site (e.g. shallow, clear, enclosed bay) allow for a precise estimates of the number of

marked individuals (Cantor et al., 2012). Group size in the Bay of Islands is very variable,

and the study site is highly convoluted ranging in water depth and clarity, and therefore

following methods by Williams et al. (1993) was deemed more appropriate. The

proportion of marked dolphins and its variance were estimated as:

∑

( ) (∑

) ⁄

Where is the number of photographs with marked dolphins; is the total number of

excellent and good quality photos taken during the ith sampling day and k (=28) is the

total number of sampling days for each ⁄ .

2.3.4 Total population size

The total population size of bottlenose dolphins in the study area within each

primary session was calculated by dividing the estimated population size by the

proportion of identifiable individuals in the groups encountered:

⁄

Where is the abundance of marked dolphins. The variance (var) and standard error

(SE) of were estimated (Wilson et al 1999) by:

( ) ( ) ( ⁄ ⁄ )

( ) √

Log-normal 95% confidence intervals were calculated (Burnham et al 1987) with a lower

limit of ⁄ and ⁄ where

( ⁄ √ [ ( ) ])

Where ⁄ is the normal deviate, = 0.05 and CV is the coefficient of variation.

32

2.4 Results

2.4.1 Survey effort and data sets

A total of 67 surveys were conducted to collect photo-identification and habitat use data in

from March 2009 to January 2010, resulting in 437.88 hours of effort (Hartel, 2010) (table

2.3). This data-set contained a total of 1,271 sighting records (including replicates of the

same individual) of 65 groups, representing 155 individuals. Between February and

December 2012 a total of 13 surveys were conducted from an independent research vessel

resulting in 65.65 hours of effort. This data-set contained a total of 313 sighting records of

15 groups, containing 72 individuals. The overall data set from 2009-2012 included a total

of 1,349 sightings of 136 uniquely marked dolphins.

Seven individuals sighted that were not assigned ID numbers in 2009 were added to the

catalogue and 10 new individuals were added in 2012 as there was a high quality

photograph (good and excellent) available and each bare sufficient markings (D1 and D2

rating).

The data set used for the RD analysis included five primary sessions in 2009, and three

primary sessions in 2012. Across these eight primary sessions there were a total of 772

sightings of 115 individuals from a total of 29 survey days. Primary sessions contained

between 3 and 5 secondary sampling days and were separated by a minimum of 14 (0.04

decimal years) and 794 (2.18 decimal years) days between their mid dates.

Table 2.3 Summary of photo-identification effort conducted in the Bay of Islands from 2009-2010

(Hartel, 2010) and 2012.

2009-2010 2012

Photo-ID surveys 67 13

Hours on survey 437.88 65.65

Total groups encountered 65 15

Total no. dolphins 155 72

Uniquely marked dolphins 128 56

Total sightings 1,271 313

33

2.4.2 Photographic data analysis

From the 496 catalogued dolphins, 18 individuals were removed from the catalogue as

either their best photograph or mark distinctiveness did not meet the criteria for MR

analysis. This resulted in 478 catalogued individuals that were used to match photographs

against. After the photographic grading process 112 individuals remained in the data set

and were in the analysis.

2.4.3 Goodness of Fit tests

Results from CloseTest indicated that the assumption of closure was not violated for each

of the selected primary sessions (table 2.4). The results from the model selection

procedure in CAPTURE can be seen and a mixture of closed model types were selected

for the primary sessions.

Table 2.4 Summary of the RD for each primary session from 2009-2012. The CAPTURE model

selection criterion (MSC, as implemented by MARK; White & Burnham, 1999) was used to evaluate

the most appropriate closed model given the data. = time variation in capture probability and a

behavioural response to first capture; = a behavioural response to first capture; = time variation

in capture probabilities and; = equal probability of capture for all dolphins.

Year Start date End date Occasions ID captured Closure

test MSC

2009 19-Jun 24-Jun 3 30 0.063

20-Jul 30-Jul 3 44 0.538

8-Aug 22-Aug 5 52 0.910

20-Nov 25-Nov 4 55 0.182

4-Dec 10-Dec 5 75 0.982

2012 8-Feb 10-Feb 3 21 0.563

10-Jul 13-Jul 3 22 0.062

10-Dec 12-Dec 3 33 0.521

Goodness of Fit tests conducted in U-CARE indicated some over-dispersion

47). The bootstrapping

method indicated a slightly higher ( ) value (1.94). There was no evidence of an effect of

trapping on survival (Test 3.SM), and while the effect of transience (Test 3.SR) was

rejected, the P-value was low (0.08; not shown) suggesting that there were a number of

animals that were only seen once (Table 2.5). There was evidence of a trap happy effect in

34

response to first capture (Test 2.CT), but no evidence that it lasted more than one interval

(Test 2.CL).

Table 2.5 Results from Goodness of Fit tests run in U-CARE for the CJS open model. Overall, Test 3

looks for violations of the assumption that all marked animals have the same probability of surviving

between occasions. Test 2 looks for violations of the assumption of equal catchability. A significant

Test 2.CT test indicates that there was a trap response to first capture. A significant result in Test

3.SR indicates that a significantly large number of animals were only seen once (i.e. tests for

transience). The test statistic for both Test 3.SR and Test 2.CT are presented. Df = degrees of

freedom.

Test 3.SR Test 3.SM

Test 2.CT Test 2.CL Global test

P-value 0.59557 0.61489 0.0041 0.4054 0.079021 Statistic 1.7147 -2.9228 Df 6 5 5 4 20 X2 4.6036 3.5563 17.2995 4.005 29.4638

2.4.4 Closed Robust Design

Twelve models were initially included in the list of candidate models (full list in appendix

2). The model with the lowest QAICc that held 91% of the weight had constant survival,

Markovian temporary emigration, time variation in , constant , and full time

dependence in capture probability. However the estimate of apparent survival had

extremely low precision ( =0.9305; 95% CI 0.02-1.0) and therefore the model was

discarded. Three other competing models were deleted as parameters were not being

estimated reliably, suggesting that the data were not fitting well. All showed Markovian

temporary emigration. Two showed strong support for constant survival, time varying

Markovian temporary emigration, and time varying capture probability, while the other

showed time varying survival, constant Markovian temporary emigration and full time

varying capture probability. Each of these models estimates a larger number of

parameters, thus making them more complex.

Overall, there was strong evidence for full time dependence in capture probability both

within and between sessions (4, 5 versus 8, 9; table 2.6). Models with no temporary

emigration (6, 7) were rejected in favour for those with Markovian and random temporary

emigration (4 and 5) and there was more support for Markovian emigration than for

random temporary emigration. Models with constant apparent survival were favoured over

those with time varying survival (1,2,3 versus 4,5). The model with the lowest QAICc that

35

held 75% of the weight was that of constant survival, constant Markovian temporary

emigration with capture probabilities that varied both within and between sessions.

The estimate of apparent survival from the best fitting model was 0.63 (SE 0.05, 95% CI

0.53-0.72). The model suggested a Markovian temporary emigration pattern, where the

probability of an individual being available for capture in primary session i is conditional

on its availability in the primary session i-1. The probability of being off the study site

and therefore unavailable for capture during primary trapping session i, given that the

animal was present in primary session i-1 was 0.14 (SE 0.04, 95% CI 0.08-0.23. In

contrast, the probability of being off the study area in primary session i, given that the

animal was not present in the study area in primary session i-1 was 0.51 (SE 0.14, 95% CI,

0.25-0.76).

The mark ratio was estimated from 1,499 high quality photographs collected in 28 survey

days between June 2009 and December 2012. Of these, 1,300 photographs represented

distinctly marked individuals (I). From this, the mark ratio was estimated at 0.88

(SE=0.0001).

The total number of bottlenose dolphins using the Bay of Islands varied from the

lowest estimate of 24 in both February and July 2012 to the highest estimate of 94 in

December 2009 (table 2.7). Estimates were higher in all sessions, apart from June, in 2009

than in 2012.

36

Table 2.6 RD models fitted to the capture histories of bottlenose dolphins the the Bay of Islands to

estimate parameters for population size, survival, emigration and capture probability, which were

allowed to vary with time both within and between sessions. Notation: phi=apparent survival;

g”=probability of temporary emigrating off the study site; g’=probability of remaining a temporary

emigrant p=probability of capture. Where (.) = constant between sessions; (s) = varies between

primary sessions and; (t) = varies within sessions. Markovian temporary emigration = g”,g’; random

temporary emigration = g and; no g parameter is found in no movement models. In all models,

recapture probabilities were set to equal capture probabilities c=p.

Model # Model QAICc ΔQAICc AICc Weights

1 phi(.) g"(.) g'(.) c=p p(s*t) 109.5665 0 0.74743

2 phi(.) g(.) c=p p(s*t) 112.105 2.5385 0.21006

3 phi(.) g(s) c=p p(s*t) 115.5298 5.9633 0.0379

4 phi(s) g"(s) g'(s) c=p p(s*t) 120.3344 10.7679 0.00343

5 phi(s) g(s) c=p p(s*t) 122.4653 12.8988 0.00118

6 phi(.) c=p p(s*t) 148.1584 38.5919 0

7 phi(s) c=p p(s*t) 151.9146 42.3481 0

8 phi(.) g"(s) g'(.) c=p p(s) 166.1114 56.5449 0

9 phi(s) g(s) c=p p(s) 181.8586 72.2921 0

10 phi(s) g"(s) g'(s) c=p p(s) 183.7313 74.1648 0

37

Table 2.7 Abundance estimates of distinctly marked individuals and corrected abundance estimates

taking into account the proportion of unmarked dolphins in the Bay of Islands for the best fitting

model (constant survival, constant Markovian temporary emigration and full time variation in

capture probabilities). = abundance estimate of marked individuals; SE = standard error;

=total abundance estimate.

Date SE 95% CI SE 95% CI

Jun-09 38 6.26416 25-50 43 7.15 29-57

Jul-09 52 5.53016 41-63 59 6.11 47-71

Aug-09 61 5.49041 51-72 69 6.31 57-81

Nov-09 65 6.0048 53-77 74 6.89 61-88

Dec-09 83 4.60321 74-92 94 5.35 84-105

Feb-12 21 0.00000 22-22 24 0.24 24-24

Jul-12 22 0.00004 22-22 25 0.29 24-26

Dec-12 34 2.41597 29-39 39 2.81 33-45

2.5 Discussion

2.5.1 Capture probabilities

While capture probabilities fluctuated between primary sessions, they were highest for

primary sessions in 2012. Results also suggested that individuals captured for the first

time in each session showed different probabilities of being recaptured. Constant capture

probabilities between sessions are only expected when recapture is similar for all

individuals (Cantor et al., 2012). Two explanations may account for the observed variation

1) differences in sampling effort between years and 2) differences in individual site

fidelity. It is unlikely that differences in sampling effort resulted in the observed variation

in capture probability as the aim of both research periods was to photo-identify all

individuals within the bay in each survey. More likely, capture probabilities were probably

influenced by differences in habitat use and site fidelity between individuals. The Bay of

Islands is characterised by core users, occasional visitors and transient dolphins

(Constantine, 2002; Tezanos-Pinto, 2009; Hartel, 2010). Both occasional visitors and

transient dolphins have a higher probability of being unavailable in following occasions

(Pradel et al., 1997) and therefore a higher proportion of these in a given primary session

38

will result in a lower capture probability. In general, capture probability was negatively

associated with abundance estimates; this in turn reflects the number of occasional visitors

and transients present within each session (Cantor et al., 2012). Capture probabilities were

highest for each of the three sessions in 2012, when abundance estimates were generally

lowest. Moreover, the same individuals, most of which were characterised as core users in

2009, were seen in each of these sessions. This provides further support that differences in

capture probabilities are likely to reflect residency patterns.

Heterogeneity in capture probabilities result from intrinsic differences in behaviour and

biology between individuals such as size, age, sex and ranging patterns (Otis et al., 1978).

Violations to the assumption of equal catchability are inevitable for MR studies involving

animals and can often lead to negative bias in abundance estimates (Pollock et al., 1990;

Williams et al., 2002). As a result, heterogeneity in capture probabilities could have

potentially led to underestimated abundances. Fitting RD models with heterogeneity in

capture probabilities requires a large amount of data as a larger number of parameters are

being estimated. An attempt was made to model heterogeneity in the best fitting model in

this study (appendix 3). As expected, a number of parameters were not estimated due to

sparse data. However, abundance estimates were roughly the same as models that did not

account for heterogeneity in capture probabilities (although not all were estimated), and

therefore bias appears to be minimal in this study.

2.5.2 Estimates of survival

The best fitting model suggested a constant and low apparent survival throughout the

study period (0.63, SE=0.05). This is in contrast to apparent survival rates reported for

other bottlenose dolphin populations such as Doubtful Sound, New Zealand (0.94, 95% CI

0.92-0.95, Currey et al., 2009b), Moray Firth, Scotland (0.92 95% HPDI 0.86-0.96,

Corkrey et al., 2008), and the western gulf of Shark Bay, Western Australia (0.95 95% CI

0.87-0.98, Nicholson et al., 2012). Moreover, it was low compared to previous estimates

of apparent survival for the Bay of Islands population using both the RD (0.93, 95% CI

0.91-0.94) and open population models (0.85, SE=0.02). Apparent survival is the product

of true survival and site fidelity. As a result, the complement of apparent survival includes

losses to mortality and to permanent emigration (Sandercock, 2006). For populations

where there is no or low permanent emigration apparent survival can be used as a reliable

estimate of true survival (Sandercock, 2006). For bottlenose dolphins, this situation

pertains to resident populations (e.g. Doubtful Sound, New Zealand Currey et al., 2009b;

Moray Firth, Wilson et al., 1999), or where adults utilise their natal range (e.g. western

39

gulf of Shark Bay, Krützen et al., 2004; Nicholson et al., 2012). The Bay of Islands only

represents a small portion of the population’s larger Northland range and there are varying

degrees of site fidelity between individuals (Constantine, 2002; Hartel, 2010, Tezanos-

Pinto, 2009). Therefore, it is likely that apparent survival was negatively biased due to

occasional visitors and transient dolphins that visited the Bay of Islands during the earlier

stages of the study period, and were not sighted again.

Apparent survival was low for 2009-2012 compared to 1997-2006 (Tezanos-Pinto et al.,

2013), suggesting that there were a larger number of permanent emigrants during the

current study period. However, it is important to note that the time between visits to the

Bay of Islands is long for some dolphins and therefore they may only be only regarded as

permanent emigrants relative to the study period. For example, #72 was not seen in the

bay from 1994 until it reappeared in 2009 (Hartel, 2010), and was seen on a number of

occasions throughout 2012. Moreover, it is potential that some individuals use the bay so

infrequently that sightings were not made (i.e. fell between primary sessions), even though

they may have visited on during the study period. This may especially be true for this

study, as there were two more primary sessions in 2009, followed by a two year gap.

Additionally, one primary session from 2012 where a number of individuals sighted in

2009 could not be used as the assumption of closure was severely violated (P-

value<0.0001), which may have negatively biased apparent survival.

A shift in home range by some dolphins during the study may have contributed to the low

apparent survival. Shifts in home range have been observed for bottlenose dolphins due to

anthropogenic factors and environmental variables. For example, dolphin watch tourism

has been found to displace dolphins in Shark Bay, Australia (Bejder et al., 2006b) and

Fiordland, New Zealand (Lusseau et al., 2006). A northward extension in the range of

bottlenose dolphins along the coast of California was an indirect effect of an El Nino event

in 1982-1983, which caused an increase in the SST (Wells et al., 1990). This was thought

to change prey distribution, which induced a shift in the dolphin’s home range (Wells et

al., 1990). There was a notable change in the individuals that use the Bay of Islands in

2009 (Hartel, 2010) compared to 1996-2000 and 2003-2005 (Constantine, 2002; Tezanos-

Pinto, 2009). It is therefore possible that this transition is still occurring, with fewer

individuals utilising the Bay of Islands as part of their range, as a number of dolphins that

were seen throughout 2009 were not seen in 2012. Increased mortality may have also

contributed to the low apparent survival estimate. Determining the magnitude of its effect

on apparent survival estimates is difficult as carcass recovery is rare, mainly coming from

40

beach-cast carcasses (Tezanos-Pinto et al., 2013). Analysis of the stranding database did

not indicate an increase in mortality for the study period of 1996-2005 (Tezanos-Pinto.,

2013). However, 14 dolphins that were core users during 1997-1999 were not sighted in

2002-2006; five of these were confirmed dead and analysis of Hauraki Gulf catalogue

revealed that they had not been sighted there, suggesting they had either permanently

emigrated or died (Tezanos-Pinto., 2013). Differences in survival probabilities may arise

due to intrinsic differences such as age, sex and size, which may affect survival estimates

(Lebreton et al., 1992). Age-specific mortality rates have been reported for bottlenose

dolphins, with higher mortality rate of calves (see Currey, 2009b). However, analysis in

this study was mainly restricted to the adult population and therefore it is unlikely that

differences in mortality among age-classes have biased the survival estimate.

The ranges of some Northland bottlenose dolphins is large, with individuals travelling up

to 720km. Comparisons between photo-identification catalogues in the Bay of Islands and

the Hauraki Gulf found that 59% of individuals in 2000-2003 (Berghan et al., 2008) and

65% of individuals in 2000-2006 have been sighted in both locations (Tezanos-Pinto,

2011). It is interesting to note the increase in the percentage of population sighted between

the regions and another comparison should be undertaken in light of the results presented

in this chapter. No other formal studies have been conducted outside of these areas. It is

therefore essential that effort is invested into photo-identifying bottlenose dolphins in

other areas of their range to untangle the relative importance of true mortality and

permanent emigration on apparent survival.

2.5.3 Temporary emigration

There was clear evidence of temporary emigration of bottlenose dolphins in the Bay of

Islands, and movement patterns seemed to follow a Markovian pattern. Strong evidence of

temporary emigration found in this study suggests that the Bay of Islands only represents a

portion this population’s range, which is supported by previous photo-identification work

(e.g. Berghan et al., 2008). Markovian movement indicates that an animal remembers that

it is off the study site and its return is a result of some time-dependent function (Pine et al.,

2003). Temporary emigration away from the sampled area was relatively low (14%),

while the probability of remaining off the study area was moderate (51%). This finding is

interesting as temporary emigration patterns were found to be random for bottlenose

dolphins in the Bay of Islands from 1997-2006, suggesting that there has been a further

change in the way that dolphins utilise the area. The temporary emigration rates in this

study were similar to a mostly resident population of bottlenose dolphins in a coastal

41

lagoon system in Southern Brazil, where the occurrence of transient animals is rare

(Daura-Jorge et al., 2012). The Bay of Islands population has been characterised by a mix

of core users, occasional visitors, and transients since it was first investigated in 1996

(Constantine, 2002; Tezanos-Pinto, 2009; Hartel, 2010). However, a shift from random

movement to Markovian, a low probability of temporarily emigrating, and a moderate

probability of remaining a temporary emigrant suggests that the Bay of Islands is now

predominantly used by a smaller number of individuals (i.e. the core users), and becoming

less important for many dolphins who only visit the area at specific times.

Seasonal shifts in home range of bottlenose dolphins are common and are usually in

relation to water temperature, which is thought to be a result of shifts in prey distribution

or to fulfil thermal requirements (Connor et al., 2000). The sub-tropical East Auckland

Current moves into the Bay of Islands around December, causing a sharp increase in SST,

which in turn induces a change in fish species within the area. Bottlenose dolphins in the

Bay of Islands have consistently displayed seasonal movements since 1996, which is

likely to be from shifts in fish distribution (Hartel, 2010). It is likely that the change in

SST, which brings different fish species into the bay in the warmer months has some

influence over when individuals visit. However, the sub-tropical East Auckland Current is

not a new phenomenon and therefore cannot be used as an explanation for the change

from random to Markovian movement observed for the Bay of Islands population. An

intensive dolphin watch/swim industry runs within the bay and while effort tends to be

higher in the summer months it is a year round operation. Moreover, private boat traffic is

also generally higher in the warmer months. Habitat choice by bottlenose dolphins is

strongly linked to predator avoidance and in some respects behavioural responses to boat

activity can be seen in the same light. Predation and non-lethal human disturbance create

similar trade-offs between avoiding the perceived risk and engaging in activities such as

feeding and mating that ultimately increase fitness (Frid & Dill, 2002). Increased

avoidance with age as well as decreased resting behaviour in response to tourism has been

found for dolphins in the Bay of Islands (Constantine, 2001; Constantine et al., 2004).

Boat avoidance has also been documented for other populations that are subject to tourism

e.g. Doubtful Sound, New Zealand (Lusseau et al., 2003). In Milford Sound, New

Zealand, the use of the fiord by bottlenose dolphins was negatively correlated with boat

traffic (Lusseau, 2005). The authors suggested that it was more beneficial to avoid the area

all together when the energetic cost of boat avoidance became too high. It has been

suggested that dolphin watch/swim tourism contributes to calf mortality in Doubtful

42

Sound (Currey et al., 2009b). Similarly, Bejder (2005) showed that there was a negative

correlation between cumulative boat exposure and reproductive success of females in

Shark Bay for bottlenose dolphins (Bejder, 2005). In the Bay of Islands, 42% of calves die

before one year of age (Tezanos-Pinto, 2009), which is high compared to other reported

figures (e.g. Wells & Scott, 1990; Mann et al., 2000). Whether or not tourism has directly

increased calf mortality in the Bay of Islands is unknown. However, if female

reproductive success is lowered as a result of it, then mothers may actively avoid the area

when pressure from tourism is highest. It is therefore possible that the shift to a Markovian

temporary emigration pattern in the Bay of Islands reflects a temporal balance between

boat avoidance, offspring protection and food acquisition; individuals visit the bay only

when the energetic benefits of feeding outweigh the costs associated with tourism.

2.5.4 Estimates of abundance

Abundance estimates varied between primary sessions, which reflect how individuals

from the Northland population use the study area over the year as opposed to true changes

in size through mortality and recruitment. For 2009, the number of dolphins using the Bay

of Islands was lowest in the colder months of June and July, started to increase in August

and was highest at the beginning of the warm season. Numbers were generally lower for

2012 and most of the same individuals were seen over the year. This is not a result of

differences in survey effort, as the aim of both researchers (Hartel, 2010; current study)

was to photo-identify all dolphins within the bay. Moreover, it is important to note that

while inter-annual differences in survey effort may lower the precision of abundance

estimates, the estimates themselves are relatively robust to survey effort (Nicholson et al.,

2012). Seasonal changes in abundance are common for coastal cetaceans and are related to

changes in local conditions, which create more favourable conditions for foraging, safety

from predators, rearing offspring, and mating (Barco et al., 1999; Irvine et al., 1981; Shane

et al., 1986; Wilson et al., 1997). Interestingly, abundance increased substantially before

the warm season and numbers were relatively low in February, when SST is high and

therefore conditions should be most favourable. However, February is generally a busy

month for dolphin watch/swim tourism, and therefore it may not be beneficial for many

dolphins to stay in the bay throughout the entire warm season (section 2.5.3).

Abundance estimates were similar to those reported for the population by Tezanos-Pinto et

al. (2013) after there had been a significant decline in population size. Whether or not the

population is still in decline is unknown; however, there is certainly no indication that it is

recovering. Overall, the encounter ratio of dolphins in the Bay of Islands has increased

43

over time; 0.69 in 1997-1999, 0.87 in 2003-2006, 0.80 2009-2010 (Hartel, 2010) and 0.93

2012 (current study). Interestingly, abundance estimates were lowest in 2012 even though

encounter ratio was highest. This supports Tezanos-Pinto et al. (2013), who suggested that

this pattern indicates that fewer dolphins were using the bay more regularly.

There has been a notable change in the individuals that use the bay, with only a small

proportion of individuals that were core users sometime from 1996-2006 still classified as

core users in 2009-2010. Moreover, of the 30 individuals classified as core users during

2009-2010, only 16 were seen on a regular basis, 10 were never seen, and four were seen

only a few surveys. This combined with the observed change in habitat use within the bay

(Hartel, 2010), the low probability of temporarily emigrating, the moderate probability of

remaining off the study site, and the low apparent survival estimate (i.e high number of

permanent emigrants) provides evidence that the decline in local abundance is in part due

the discontinued use of the Bay of Islands by a number of individuals. Apparent decline in

local abundance has been observed in other coastal population of bottlenose dolphins,

which has mainly been attributed to human-related disturbance such as tourism (Bejder et

al., 2006a) and development (David, 2006).

A behavioural response to first capture was detected between sessions, indicating the

assumption of equal catchability was violated. This may indicate that some dolphins are

tolerant of the research boat (Constantine, 2002), or that differences in sampling effort

within between years led to some individuals being sampled more frequently. However, as

previously stated, the goal of both researchers was to photograph all individuals within the

bay in each survey and therefore it is unlikely that differences in effort caused this effect.

Additionally, the use of good quality cameras, with high zoom lens (100-300mm) allows

individuals to be captured at far distances. Moreover, it is generally accepted that photo-

identification techniques do not induce a behavioural effect as there is no any physical

handling of the animals (Pollock et al., 1990; Williams et al., 2002). The trap-happy

response was more likely an artefact of the differences in site-fidelity, social structure, and

the Markovian pattern of movement. Core users are sighted within the bay more

frequently than others (Constantine, 2002; Tezanos-Pinto, 2009; Hartel, 2010), and

associations tend to be stronger between them than with others (chapter 3). Their more

regular use of the bay, along with their preferential associations, most likely led to some

individuals being captured more than others. While the TEST 2.CT goodness of fit test

was originally designed to detect a behavioural response to first capture, Schaub et al.

(2004) found that it was also useful for detecting Markovian temporary emigration.

44

Markovian temporary emigration patterns were evident in this study, which may have

contributed to the significant result observed for TEST 2.CT. Regardless, estimates of

abundance are negatively biased when trap-happiness is not accounted for and this affect

may be present in the results.

2.5.5 Limitations

Using the RD to estimate population parameters is advantageous as it allows for a larger

number of parameters to be estimated with greater precision. However, they represent a

complex set of models with a large number of parameters and as a result require more data

to generate reliable estimates. In this study, there was strong evidence for time variation

temporary emigration, mainly for the probability of temporarily emigrating off the site

given the animal was available in the previous primary session (appendix 2). While

estimates of were similar between most sessions, the probability of being a temporary

emigrant between session between December 2009 and February 2012 was 0.78 (95% CI

0.47-0.94) suggesting that a large number of individuals temporarily emigrated between

this period. It is important to note that under these conditions, the probability of remaining

a temporary emigrant was substantially higher 0.92 (95% CI 0.64-0.99), as was apparent

survival 0.93 (0.02-0.1). However, the precision of apparent survival was extremely low,

with the confident intervals hitting the boundaries, and therefore the model was discarded.

These results suggest that important information may have been lost when temporary

emigration was kept constant, which in turn affected the apparent survival estimate.

Nonetheless, abundance estimates were similar between the two models and so for this

purpose the best working model was sufficient. While time varying temporary emigration

was strongly favoured, the results in both models suggested the same thing; the probability

of remaining a temporary emigrant is higher than the probability of temporary emigrating

given that the animal was seen in the previous session.

2.5.6 Summary

This study found that apparent survival rates were low for bottlenose dolphins in the Bay

of Islands compared other reported populations, even compared to the last estimate from

1997-2006. True mortality rates are unknown and therefore it is difficult to separate its

effect from permanent emigration. However, models with time variation in temporary

emigration led to more realistic estimates of survival. These models are highly

parameterised and this complexity reduced the precision of the model estimates to the

extent they were not reliable.

45

A change from random to Markovian temporary emigration has been observed, with a

higher probability of individuals staying away from the bay than for them leaving,

suggesting that a small portion uses the bay regularly, with others visiting less regularly

and only at specific times. When individuals visit most likely reflects a balance between

engaging in fitness enhancing activities when conditions are favourable, and avoiding the

negative impacts imposed by tourism. Fluctuations in abundance estimates reflect the

temporary emigration characteristic of this population, and clear increases in numbers

from August to December support Markovian movement. Abundance estimates were

similar to, if not slightly lower, than those found by Tezanos-Pinto et al. (2013) after the

population had undergone a significant decline, suggesting that the population may either

still be declining or that it is remaining stable.

46

3 Social Structure

3.1 Introduction

Understanding the social structure of animal populations is of importance as it influences a

number of facets of their population biology including information transfer (Lusseau et al.,

2003), gene flow and population genetic structure (Storz, 1999), disease transmission,

population growth, and habitat use (Axelrod & Hamilton, 1981; Wolf et al., 2007). As a

result, social structure influences both ecology and evolution and therefore knowledge of

it is not only important for theoretical reasons, but also for guiding conservation and

management decisions (Altizer et al., 2003; Chilvers & Corkeron, 2001; Frère et al.,

2010).

For cognitively advanced mammals with complex social systems, a robust framework is

required to describe inter-population variation in social structure, and in turn understand

the specific ecological forces acting on each population (Myers, 1983; Whitehead, 1995;

Whitehead & Dufault, 1999). Hinde (1976) developed a framework for studying social

structure, which has been applied to socially complex animals such as elephants

(Wittemyer et al., 2005), chimpanzees (Mitani, 2009) and a number of cetaceans

(Karczmarski et al., 2005; Whitehead, 2008a). Within this framework, the content, quality

and patterning of interactions between individuals define the relationships between dyads,

whilst the nature, quality and patterning of these relationships define the social structure.

Data on interactions between individuals are therefore essential as they form the basic

building block from which relationships, and thus societies are formed.

Because social structure and group dynamics are based on the same ecological theories, it

is important to consider them simultaneously. Ecological factors such as risk of predation,

resource availability, and access to mates are thought to promote group living; individuals

living in groups benefit from factors such as predator defence and increased foraging

success (Alexander, 1974; Krause & Ruxton, 2002; van Schaik et al., 1983). However, as

group size increases, a number of costs associated with group living arise (e.g. competition

for resources, disease transmission). As a result, it can be advantageous for individuals

living in groups to adopt a flexible social system. In fission-fusion societies, individuals

live in small groups, which change in size and composition on a regular basis (Couzin,

2006). The ability to adjust group dynamics in response to factors such as predator risk

and food availability allows individuals to maximise the benefits of group living

47

depending on the environmental conditions, while minimising the costs (Connor et al.,

2000). Fission-fusion societies are characteristic of a number of primates (van Schaik et

al., 1983), elephants (Archie et al., 2006), and delphinids (Bräger, 1999; Parra et al., 2011;

Pearson, 2009).

For cetaceans, visual estimates of group size and demographic composition are achievable

due to their surface activity. Determining the content of interactions between individuals

however is difficult due to the limiting nature of the aquatic environment. As a result,

associations between two individuals (i.e. dyads) are used in place of interactions to

investigate social structure under the premise that the vast majority of interactions occur

when two individuals are within close proximity (Whitehead, 2009). Photo-identification

methods (chapter 2) enable researchers to identify individuals from their unique markings

(Würsig & Würsig, 1977; Würsig & Jefferson, 1990), and thus collect association data.

This approach has been applied to a number of cetaceans including killer whales (Tosh et

al., 2008); pilot whales Globicephala melas (Ottensmeyer & Whitehead, 2003); spinner

dolphins Stenella longirostris (Karczmarski et al., 2005); and bottlenose dolphins

(Bouveroux & Mallefet, 2010; Connor et al., 2000) and this has allowed detailed

descriptions of their social systems.

A number of on-going studies on coastal bottlenose dolphin populations have not only led

to a more holistic understanding of the species, but they have enabled comparisons of the

social structure and group dynamics to be made between populations (Connor et al.,

2000). To date, all bottlenose dolphin populations that have been studied live in fission-

fusion societies (Connor et al., 2000). Bottlenose dolphins are a long-lived species (~50

years), exhibit seasonal breeding, and they have a gestation period of 12 months, with

three to four year intervals between births (Connor et al., 2000). Breeding is centred in

spring/summer when conditions are more favourable for females to invest energy into

caring for their calf (Mann et al., 2000). Group composition and association patterns

however vary considerably. Age and sex specific association patterns vary widely between

populations depending on the major socio-ecological benefits in behavioural activities

such as mating, predator defence and foraging (Gowans et al., 2008). There are also a

number of other non-exclusive factors that influence the association patterns of bottlenose

dolphins including kinship (Möller et al., 2001), the degree of isolation from other

communities (Lusseau et al., 2003), and anthropogenic activities (Ansmann et al., 2012).

Due to this, it is necessary to conduct detailed studies of individual populations in order to

understand their ecology (Campbell et al., 2002). Similarly, group composition and social

48

structure can change over time within a population, as a result of shifts in environmental

conditions, which may alter the population’s structure (Connor et al., 2000). It is possible

to detect these changes by maintaining baseline data sets that allow individual

identification.

Bottlenose dolphins in the Bay of Islands form part of the larger Northland population

(section 2.1.3). Past research on the social structure in the Bay of Islands suggests that the

population is characterised by a fission-fusion society where groups are rarely stable for

more than a few days, and often leave the bay only to be replaced by a new group

(Mourão, 2006). Associations appear to exist at two levels; casual acquaintances and long-

lasting companionships between and within sexes (Mourão, 2006). Group size is highly

variable, ranging from 1 and 60, with a median of 15 (Constantine, 2002; Hartel, 2010;

Mourão, 2006; Tezanos-Pinto, 2009). Groups containing calves are larger than those

without, which is a common pattern seen for bottlenose dolphins internationally (Bearzi et

al., 1997; Campbell et al., 2002; Mann et al., 2000). There are no resident individuals, but

rather there is a mix of core users, frequent visitors and transient individuals (Constantine,

2002; Tezanos-Pinto, 2009). The bay is a popular coastal area for human recreation and as

a result of this, the population present is exposed to a range of anthropogenic activities. In

particular, they are subject to an intensive dolphin watch/swim industry, which has a

significant effect on their behaviour (Constantine, 2001; Constantine et al., 2004).

There have been a number of changes to the Bay of Islands population over the past two

decades. Notably, there has been in a change in the individuals that are core users of the

bay, a shift in habitat use and a marked decline in abundance (Hartel, 2010; Mourão, 2006;

Tezanos-Pinto et al., 2013). Due to the decline in abundance and the change in the core

users of the area, an assessment of the social structure is of importance as the loss of

individuals can cause social disruption, which may have important implications for the

conservation of the population (Augusto et al., 2012; Wade et al., 2012). Gaining

knowledge of the current demographics of the population is also of importantance for

better management of the population (Kogi et al., 2004).

The aim of this chapter is to; 1) provide a current description of group size and age-class

composition in the Bay of Islands, 2) describe the current social structure of bottlenose

dolphins in the Bay of Islands and determine whether there are sex-specific patterns to

associations.

49

3.2 Methods

3.2.1 Surveys and photo identification

Boat-based photo-identification surveys were conducted between March 2009 – January

2010, January – April 2011, and February – December 2012 following the methods

described in chapter 2.2.2.

3.2.2 Group size and age class composition

Once a group of dolphins was located, group size and age-class composition were

estimated visually. For each group a minimum, maximum and best estimate of the total

number of dolphins was recorded. Dolphins were placed into one of four relative age

categories: neonates, calves, juveniles or adults (table 3.1). A dolphin seen in close

association with either a neonate or a calf on two or more independent occasions was

assumed to be the calf’s mother. If a known mother was seen without either her neonate or

calf on two subsequent occasions, it was assumed that her neonate or calf was deceased.

This is a valid assumption as calves are dependent on their mothers for nourishment and

therefore remain in close proximity to her at all times (Mann et al., 2000). Group size and

age-class estimates were later validated and corrected through photo-identification

analysis (chapter 2). Because bottlenose dolphins are not sexually dimorphic the sex of

individuals was determined via opportunistic sightings or photographs of their genital area

(Smolker et al., 1992). Additionally, females were identified through association with

either a neonate or calf.

Because mean values are often skewed by outliers, both mean and median group size

values were reported. A Shapiro-Wilcoxon test for normality rejected the hypothesis that

group size was normally distributed (W=0.67, P=0.0001) therefore non-parametric tests

were used for all group size analyses. Movements of calves and neonates are not

independent of their mothers and therefore they were excluded from the total group size

estimates (3.4.1).

50

Table 3.1 Definitions of the four relative age classes for bottlenose dolphins (Constantine, 2002).

Neonate

Categorised based on the presence of white dorso-ventral foetal folds along their sides. Neonates have poor locomotion skills and are often uncoordinated upon surfacing to breathe (Mann & Smuts, 1999). Life stage duration: up to 3 months

Calf

Defined as dolphins that are approximately one-half or less (1-1.5m in length) the adult size. Calves are closely associated with their mother and are often observed swimming in infant position (in contact underneath the mother) (Wells et al., 1980; Mann & Smuts, 1999). Life stage duration: 3-4 years

Juvenile

Defined as dolphins that are approximately (2-2.5m in length) two-thirds the size of an adult. Juveniles are often closely associated with their mother but are never observed in infant position (Mann & Smutts, 1999). This indicates that they have been weaned (Mann et al., 2000). Life stage duration: until sexual maturity (for females, often indicated by close association with calf).

Adult All dolphins that are fully grown (3-3.5m in length)

3.2.3 Photographic analysis

Photographs from each survey were sorted by individual into separate folders. Individuals

with clean fins or small, indistinguishable nicks were excluded from the analysis to

minimise misidentification rates and reduce bias. The best quality photograph of each

distinctly marked individual was compared to the Bay of Islands fin catalogue. Secondary

features including the shape of the dorsal fin, tooth rake marks, scratches, scars, wounds,

and pigmentation on the dorsal side of the body were used in conjunction to match

individuals (Würsig & Jefferson, 1990).Whist it is important to include as much

information as possible concerning associations between individuals, it is also crucial that

misidentification rates are minimised in order to obtain an accurate picture of the true

social system. Therefore, individuals were only included if they could be unequivocally

matched from their photo. If an individual could not be matched to the existing catalogue

by at least two researchers it was assigned a new number.

3.3 Data analysis

To determine the social structure of bottlenose dolphins in the Bay of Islands, data from

2009-2012 were analysed together to obtain a large sample size and extend the length of

time over which the associations were analysed. Analyses were carried out using the

software program SOCPROG 2.4 (Whitehead, 2009). To assess whether the association

51

data were satisfactory for describing the social structure of bottlenose dolphins in the Bay

of Islands a Pearson’s correlation was calculated between the observed (true) and

estimated (predicted) association indices (AIs). A coefficient of variation was calculated to

measure the degree of social differentiation within the population. AIs were calculated for

each dyad in the data set to measure the strength of associations. Permutation tests were

run to determine whether the individuals were associating at random or exhibited preferred

or avoided associations. Finally, standardised lagged association rates (SLAR) were

calculated and models describing the relationships were fitted to assess the temporal

stability of associations between individuals within the population.

3.3.1 Accuracy of social representation

A Pearson’s correlation coefficient between the true AIs and the observed AIs was

calculated to determine whether the association data were of good enough quality to detect

the true social system of the population. An r value of approximately 0.4 indicates that the

data provides a slightly representative pattern; 0.8 a good representation; and 1 an

excellent representation (Whitehead, 2008b).

To investigate social differentiation, the degree to which a dyad within the population

differs in their probability of association, the coefficient of variation of the true association

indices was calculated (Whitehead, 2008b). Values of less than around 0.3 indicate

relatively homogenous societies, greater than 0.5 indicate well differentiated societies, and

values of greater than around 2.0 are represented by extremely differentiated societies

(Whitehead, 2008a; 2008b).

3.3.2 Association indices

For the analysis, associations were defined based upon group membership (section 2.2.2.)

and the sampling period was set to four days to increase the power of permutation tests

(section 3.3.3) to detect preferred associations (Whitehead, pers. comm.). In populations

where a number of groups are seen within a single day, using a sampling period of one day

is appropriate to detect preferred associations. However, in the Bay of Islands where

usually only one group is found a day permutation tests (permuting associations) have

very little power (Whitehead, pers. comm.).

AIs can become negatively biased when all individuals within a group are not photo-

identified (Parra et al., 2011). Therefore, groups with less than 50% of individuals that

were successfully photo-identified were removed from the analysis. Analysis was further

limited to individuals that were sighted on four or more times (Whitehead, 2008a). This is

52

common practice in association work, which represents a compromise between including

as many individuals as possible and limiting bias created by the misidentification of

individuals (Bejder et al., 1998; Parra et al., 2011).

The half weight index (HWI) was used to measure the strength of dyadic associations. A

value of 0 indicates no association at all and 1 indicates that the pair is always associated.

The HWI is designed to compensate for unaccounted associates within a group (Cairns &

Schwager, 1987). This is where association between A and B:

⁄

Where X is the number of sampling periods both A and B were seen together in the same

group; Ya is the number of times individual A was seen without B; and Yb is the number

of times B was seen without A. The Bay of Islands only represents a portion of the

populations range, and usually only one group of dolphins is present in the bay on any day

(Constantine, 2002) therefore any given pair of individuals is more likely to be sighted

apart than together.

3.3.3 Preferred or avoided associations

To determine whether variation in associations was due to preferred or avoided

associations or a result of chance alone, permutation tests were conducted in which the

association data is randomised while holding important features of the data constant

(Bejder et al., 1998). From the three methods available to run permutation tests, ‘permute

associations within samples’ was chosen. This test uses a modification of the Bejder et al.

(1998) procedure keeping the number of associations of each individual in each sampling

period constant. This method controls for demographic effects (i.e. birth, death,

immigration and emigration) over the study period as well as differences in gregariousness

between individuals (Whitehead, 2009). In the permutation test, a distribution of test

statistics for HWI values is produced from which the P-value is calculated. Because a new

randomly generated data matrix is always generated from the previous one, results are not

independent and so the P-value is generally conservative and results are positively biased

(Manly, 1995). To ensure that results were not biased, the number of permutations was

increased until the P-value stabilised (Bejder et al., 1998). The difference between the

mean standard deviation of the observed HWI values and the expected (permuted) HWI

values was used to determine whether the variation in association indices was due to long-

term preferred or avoided associations or by chance (Whitehead, 2008b); with a

significantly larger observed standard deviation indicating non-random associations.

53

HWIs were then calculated for each dyad after each random permutation of the data. The

expected HWI for each dyad was the average HWI of all permutations and was compared

to the observed HWI (Lusseau et al., 2003). Only individuals with a HWI of 0.5 or greater

were focused on to limit analysis to strong associations. A Mantel’s test using 1000

permutations was used to determine whether HWIs within and between sex classes were

different.

3.3.4 Temporal patterns of associations

To investigate the temporal patterning of relationships between individuals, the lagged

association rate (LAR) and the null lagged association rate (NLAR) were determined for

females, males, females and males, and all individuals (i.e. including dolphins of unknown

sex). The LAR estimates the probability that if two individuals are associating at a given

time (i.e. start of the study period), they will still be associated several time lags later

(Whitehead, 1995). In contrast, the NLAR represents the expected value if associations are

random. Both the LAR and the NLAR were both standardised as all members of a group

were not always identified during a sampling period and therefore not all true associations

were recorded (Parra et al., 2011; Whitehead, 1995). This is done by taking the number of

associates an individual is seen with at each specific time lag into consideration.

Standardised rates are an estimate of the probability that if two individuals are associated

at a given time, the second is a randomly chosen associate of the first after a specified time

lag. The standardised lagged association rate (SLAR) was compared to the standardised

null lagged association rate (SNLAR) to determine whether the temporal patterning of

associations between individuals were significant. All individuals were included in the

analysis regardless of how many times they were sighted to avoid positively bias the

SLAR (Baird & Whitehead, 2000; Lusseau et al., 2003) and the sampling period was set

to one day to obtain interpretable results (Whitehead, pers. comm.). Standard errors were

estimated using jackknife methods (Efron & Gong, 1983). The observed temporal

association pattern for all individuals was then compared to four different models to

investigate the types of associations within the population. The models look for different

levels of temporal associations as well as combinations of them to best describe the

temporal patterning (Whitehead, 2008a). The models used were (1) constant companions:

stable associations over extended periods of time; (2) casual acquaintances: irregular

associations between individuals that disassociate and then may associate at a later stage;

(3) constant companions and casual acquaintances: a combination of explanations above;

(4) two levels of casual acquaintances: where irregular associations dissociate over time,

54

but at two different rates. Model selection was carried out using the Quasi-Akaike

Information Criterion (QAIC), which corrects for over-dispersion in the count data and is

more accurate when choosing the best fitting model. The model with the lowest QAIC was

deemed as the best model. However, if there is less than two units difference between the

best and any other model(s), this provides support for the competing model and should not

be completely discarded (Whitehead, 2008a).

3.4 Results

3.4.1 Group size and age class composition

Group size and age-class composition were estimated for a total of 15 groups of dolphins

in 2012. After visual estimates of group size were corrected via photo-identification

analysis, group size ranged from 3 to 28 individuals ( = 20.9, median = 25, IQ range =

20-26). The most frequently sighted group size was 25 (26%), and 80% of groups

contained 20 or more individuals, while the remaining 20% contained less than five

individuals (fig. 3.1). No groups of 5-19 individuals were seen during the study

Of the 313 cumulative sightings of dolphins made throughout this study, two (0.5%) were

neonates, two (0.5%) were calves, 13 were juveniles (4%) and 296 (95%) were adults.

Fewer neonates, calves and juveniles were seen in 2012 compared to 2009, resulting in a

population of mainly adult individuals (table 3.2.).

Both neonates and calves were counted in association with their identified mother. As a

result the absolute number (c.f. cumulative) number of neonates and calves are reported

here. In 2012, one calf, and one neonate were documented as the offspring of two uniquely

marked female. Both of these females were known individuals (i.e. already contained in

the Bay of Islands catalogue); ID 79 was sighted with a calf on the 1st and 2

nd of

November, and ID 510 was sighted 11th

and 12th

of December, 2012.

Group size was not normally distributed and therefore only medians are reported here.

Median group size was larger in 2012 compared to 2009 (table 3.2.). In 2012, groups

containing calves (n=4) were not significantly larger than groups without calves (n=11)

(W=16, P =0.4297). This is in contrast to 2009 where groups containing calves were

significantly larger than those without (W=272.5, P<0.0001; Hartel, 2010).

55

Figure 3.1 Distribution of group sizes of bottlenose dolphins in the Bay of Islands in 2012 (n=15).

Table 3.2 Summary of the group dynamics for bottlenose dolphins in the Bay of Islands

in 2009 (Hartel, 2010) and 2012. The brackets contain the interquartile range.

Summary 2009 2012

Range 1-50 3-28

Median group size (I.Q. range) 19 (10-32) 25 (20-26)

Median group size with calves 23 (12-25) 24.5 (23-26.75)

Median group size without calves 11 (8-12) 24.5 (4-25)

Neonates (%) 1 0.5

Calves (%) 4 0.5

Juveniles (%) 8 4

Adults (%) 87 95

0

1

2

3

4

5

6

7

8

1-5 6-10 11-15 16-20 21-25 26-30

Co

un

t

Group size

56

3.4.2 Accuracy of social representation

The Pearson’s correlation (likelihood method) between the true association indices and the

estimated association indices indicated that the association data were of reasonable quality

to detect the true social system within the population r = 0.72 (SE= 0.03). The coefficient

of variation of the true association indices indicated a well differentiated population in the

Bay of Islands S = 0.52 (SE= 0.06).

3.4.3 Association indices

A total of 151 adult individuals were sighted at least once during the 2009-2012 study

period. After restricting the data set to individuals that were seen on four or more

occasions, in groups where at least 50% of individuals were photo-identified, 88

individually recognisable adult individuals were included in the analysis. This included 26

females, 7 males, and 55 individuals of unknown gender. The distribution of HWIs for all

individuals was slightly skewed to the right indicating that a number of dolphins have no

or weak associations, suggesting a high degree of fission-fusion of dolphins in the Bay of

Islands (figure 3.2). The mean observed HWI was 0.26 (SE=0.08), and HWIs were highest

between males, while lowest between females (table 3.3). However, associations between

and within female and male sex classes were not significantly different (t = -1.2801; p =

0.094).

57

Figure 3.2 Distribution of the Half Weight Index (HWI) of association for bottlenose dolphins in the

Bay of Islands. Notations: All = HWI between all individuals, F-F = female-female HWI, M-M = male-

male HWI, F-M = females-males HWI. A value of 0 indicates no association at all and 1 indicates that

the pair is always associated.

Table 3.3 Average and maximum Half Weight Indices (HWI) and standard deviations (SD) between

and within sex classes for bottlenose dolphins in the Bay of Islands.

Sex class Observed Observed mean

mean HWI (SD) of maximum HWI (SD)

All 0.26 (0.08) 0.71 (0.12)

Female-female 0.31 (0.08) 0.66 (0.13)

Female-male 0.35 (0.11) 0.56 (0.16)

Male-male 0.42 (0.06) 0.64 (0.21)

3.4.4 Preferred associations

The standard deviations of the mean HWI for all individuals, females, and females with

males were significantly higher than those from random data, indicating that individuals

within these classes showed a tendency for preferred associations (table 3.4). However,

the SD of the mean HWI for males was not significantly higher than those from random

data, which indicates no overall preferred associations.

58

A total of 57 dyads were significantly stronger than expected by chance (191 expected

significant dyads). There were 38 dyads with HWIs above 0.50 that were significantly

stronger than if by chance (Table 3.5). From these, 17 were between current core users, 10

were between occasional visitors, four between current and former core users from 1996-

2000 and 2003-2005, two between former core users, four between former core users and

occasional visitors, and one between a current core users and an occasional visitor.

Table 3.4 Observed and random Half Weight Indices (HWI ) ± standard deviation (SD) and P-values

are indicated for the random association test. The test statistic was the SD; P-values < 0.05 indicate

that the observed SD was signficantly higher than the random data.

Sex class

Observed mean HWI ± SD

Randomised mean HWI ± SD

P-value

All 0.26 ± 0.187 0.26 ± 0.181 <0.0001

f-f 0.31 ± 0.177 0.31 ± 0.172 0.0006

m-m 0.42 ± 0.171 0.42 ± 0.172 0.4948

f-m 0.35 ± 0.167 0.35 ± 0.161 <0.0013

59

Table 3.5 Dyads that had significantly strong associations (>0.5, p<0.05) compared to random

permutations. Only dyads with a HWI of 0.5 or more are displayed. Colours indicate the time period

for which the individual has been classified as a core user. Red=1996-2010; blue=2003-2010;

grey=2007-2010; yellow=1996-2000; green=1996-2005; purple=2003-2005.

Individual

catalogue # HWI

Female-female 193-126 0.53 255-126 0.67 255-169 0.6 446-215 0.71 475-454 0.75 494-454 0.59 510-454 0.67 510-475 0.8

Female-male 169-186 0.6 454-471 0.73 475-471 0.88 510-471 0.71

Female-unknown 169-520 0.63 325-4 0.77 325-503 0.74 454-519 0.52 475-516 0.74 494-519 0.55 510-516 0.58 510-519 0.63 78-519 0.76

Male-unknown 153-347 0.92 186-520 0.63 186-82 0.56

Unknown-unknown 406-118 0.8 499-347 0.84 511-347 0.78 503-4 0.64 531-406 0.53 532-406 0.5 533-406 0.5 511-499 0.75 82-520 0.59 532-531 0.67 533-531 0.67 533-532 0.5 543-533 0.55 82-469 0.82

60

3.4.5 Standardised lagged association rates

The SLAR for all individuals fell sharply within approximately three days and continued

to decline to approximately 60 days before stabilising out (fig. 3.3a). The SLAR stayed

above the NSLAR at all times, indicating the presence of long lasting preferred

relationships within the population. The large spike between approximately day 450 and

700 days reflects gaps in data collection in both 2010 and 2011. The large error bars at the

beginning of the study period most likely represent uncertainty around the short-term

associations. However, as time progressed and more information was obtained these

decreased. The best fitting model was two levels of casual acquaintances; however

confounding parameters indicated that there was a poor fit of the data to the model and it

was therefore discarded. The social-system model with the second lowest QAICc that best

described the temporal association pattern describes two types of associates: constant

companions and casual acquaintances (table 3.6). This was within four QAICc units of the

model with the lowest QAICc and therefore provided good support. At very short lag

times, the probability that an individual is still with the same associates it had at time zero

is 1 (Ottensmeyer & Whitehead, 2003). Given the standardisation of the LAR, the level at

which the expected SLAR crosses the line represents the reciprocal of the number of

casual acquaintances of a randomly chosen individual. The value of the best fitting model

at time 0 was 0.053. This suggests that the mean number of associates an individual has is

19. The level at which the SLAR curve stabilises relative to its maximum represents the

proportion of the total number of dolphins present in the short term that remained with a

given individual (Whitehead, 1995). The value of the best fitting model where the curve

levels out was 0.016, or 30% the maximum SLAR. This suggests that out of the 151

individuals, approximately 30% of individuals that were sighted at a given time with a

given individual formed a long-term association with it. The duration of associations

between casual acquaintances lasts less than a day (19.2 hours).

The SLAR follows a similar pattern between females, with a sharp decline by

approximately day three and continues to decline until around day 40 (fig. 3.3b). At

approximately day 300 the SLAR briefly comes in contact with the NSLAR. Overall, it

stays above the NSLAR suggesting that long-lasting associations occurred between

females over the study period. However, the SLAR lies close to the NSLAR indicating

that associations are less stable between females.

This pattern was also similar between females and males, with a large drop in associations

at around day three and continuing to fall until it almost comes in contact with the NSLAR

61

at around day 40 (fig. 3.3c). The SLAR remains closer to the NSLAR much like it did

between females than it did for all individuals. However, the fact that the SLAR remained

above the NSLAR indicates that long term companionships occurred between females and

males.

For males, the SLAR dropped until approximately 40 days before stabilising to some

degree (fig. 3.3d). It remained higher above the NSLAR compared to the others suggesting

that long-term associations between males were more stable.

Table 3.6 Fit of four social-system models to the standardised association rate (SLAR) for bottlenose

dolphins in the Bay of Islands. Notation: CC = constant companions; CA = casual acquaintances. The

value in bold type indicates the best fit model with the lowest Quasi-likelihood Akaike’s Information

Criterion QAICc value.

Description

of modelModel formula

Maximum-

likelihood values for

parameters (SE)

Jackknifed

standard

errors for

parameters

QAICc ΔQAICc

Two levels of CC g(τ) = a3e(– a1τ ) + a4e(– a2τ )

a1=7.4795e-5 (47.6605)

a2=1.0473 (29.5851)

a3=0.01563 (22.0907)

a4=0.03168 (0.48002)

4 62543.9764 0

CA + CC g(τ) = a2 + a3e(– a1τ )

a1=1.25 (1.5652)

a2=0.01609(0.0012597)

a3=0.03736 (0.054878)

3 62547.3375 3.4

CC g(τ) = a1 a1=0.016343 (0.0013367) 1 62584.9803 41

CA g(τ) = a2e(– a1τ )

a1=-3.1657e-05 (8.1019e-05)

a2=0.01615 (0.004252)2 62586.0751 42.1

62

Figure 3.3 Standardised lagged association rates (SLAR) for: (a) all individuals; (b) among females; (c) between females and males and; (d) among males. Each SLAR is

compared to the null association rate: red for (a) and green for (b, c, d). The best-fitted model for all individuals in (a) is represented by a green line.

63

3.5 Discussion

In this chapter I provide a current census on group size and composition and describe the

social structure of bottlenose dolphins in the Bay of Islands. The population is characterised

by a fission-fusion society, where individuals live in large groups and associate in a non-

random manner, without preference to whether individuals are female or male. Group size

has increased since 2009, and the demographic structure is now comprised mainly of adults.

Whilst a large proportion of associations are short-lived, long lasting companionships exist

both within and between sex classes and strong associations between individuals seem to

reflect differences in site fidelity.

3.5.1 Group size and demographic structure

The reported median in this study was larger than has been found for most coastal bottlenose

dolphin populations, where groups of 2-15 are most commonly found (Shane et al., 1986). It

has been suggested that habitat structure is one of the major influences on group size for

bottlenose dolphins as it affects the distribution of food and the risk of predation (Shane et

al., 1986). Larger group sizes have been reported along the California coast (Hanson, 1990),

San Diego (Defran & Weller, 1999) and Doubtful Sound (Lusseau et al., 2003). While the

populations in San Diego and Doubtful Sound differ in terms of their residency patterns, the

former being open and the latter being closed, both areas are similar in that food resources

are patchy in distribution due to the nature of their environment (Defran & Weller, 1999;

Lusseau et al., 2003; Matthews & Heimdal, 1979). The Bay of Islands is a large, convoluted

bay that ranges in depth and habitat type. While cooperative feeding is rarely seen here

(Hartel, 2010), individuals may increase their foraging efficiency by living in larger groups

through increased prey detection and information transfer (Bearzi et al., 1997; Lusseau et al.,

2003). Sharks and killer whales are known predators of bottlenose dolphins (Connor et al.,

2000; Heithaus, 2001; Mann et al., 2000), and their influence on group size is thought to be

substantial in some areas, such as Shark Bay, Western Australia, where predation pressure is

high (Heithaus, 2001). Whilst killer whale tooth rakes and shark wounds on dolphins in the

Bay of Islands have been reported, the frequency is low (Constantine, 2002; Hartel, 2010),

and none were observed on individuals throughout this study. While predation risk appears to

be low for this population, both sharks and killer whales visit the Bay of Islands and therefore

it is likely that they will have some influence on group dynamics, although it is likely to be

minimal.

64

The large group sizes in the Bay of Islands may also be related to group structure and

differences in site fidelity. In the Bay of Islands, an average of 1.2 groups of bottlenose

dolphins are found per day (Constantine, 2002), which is in contrast to other populations,

where multiple groups utilise the same area in time and space (e.g. Sarasota Bay, Florida and

Shark Bay, Western Australia; Connor et al., 2000). The number of core users of the bay was

last estimated at 30 individuals (Hartel, 2010). Core users tend to associate together in one

group and are often joined by occasional visitors or transients for a short time, which may in

part explain why larger groups are observed.

While previous studies show that group size has fluctuated in the Bay of Islands population

since 1996, it was larger in the current study than it has previously been. Median group size

increased from eight to 15 individuals from 1996-2000 (Constantine, 2002); it was 10 in

2003-2004 (Mourão, 2006); varied from 10.5 to 15 from 2003-2005 (Tezanos-Pinto, 2009)

and was 19 during 2007-2010 (Hartel, 2010). There was a significant change in habitat use

by dolphins in the Bay of Islands between the periods of 1996-2000 and 2007-2010 (Hartel,

2010). Among other changes, dolphins are now frequently seen in deeper waters within the

bay. Increased group size with increased water depth has been reported for other bottlenose

dolphin populations (e.g. Sarasota Bay, Wells et al., 1980), a trend that is attributed to an

increase risk of predation and a change in the distribution of prey (Connor et al., 2000). It is

therefore possible that a shift in habitat use has contributed to bigger group sizes in the Bay

of Islands.

An increased group size could also be related to the observed decline in population size

(chapter two; Tezanos-Pinto et al., 2013). While the population has shown a significant

decline, the sighting ratio (i.e. number of surveys dolphins were sighted divided by the total

number of surveys) has increased. This suggests that a fewer number of dolphins are using

the bay more regularly. Therefore it is possible that with fewer dolphins using the bay more

regularly, individuals more readily form associations with others, thus increasing group size.

This situation is similar to that seen in a resident population in the Doubtful Sound, where

population size is small and median group size is relatively high (Lusseau et al., 2003).

In this study, there was no significant difference in group size when calves or neonates were

present. This is contrary to other bottlenose dolphin populations (e.g. Campbell et al., 2002,

Defran & Weller, 1999; Fertl, 1994; Kerr et al., 2005), and a significant difference has been

consistently found in the Bay of Islands prior to this study (Constantine, 2002; Hartel, 2010;

Tezanos-Pinto, 2009). The observed habitat change may indirectly explain why groups

containing calves were not larger in this study. It is possible that the more frequent use of

65

deeper waters by dolphins has led to large group sizes in general, and thus reduced the

difference in size between groups containing calves and those not. However, it is important to

note that the sample size was small in this study (groups containing calves = 4), and therefore

caution must be taken when interpreting these results.

Assessing trends in age-class composition is important, as it provides information concerning

recruitment into the population, and therefore its viability (Bondrup-Nielsen & Ims, 1988). In

this study, the total percentage of neonates and calves sighted throughout this study was only

1%, which is low compared to prior findings for this population (4.9-11.1% in 1994 to 2005,

Constantine & Baker, 1997, Tezanos-Pinto, 2009; and 5% from 2007 to 2010, Hartel 2010).

Differences in age-class structure between years can result from pulses of birth rates between

years (Haase & Schneider, 2001), and therefore it is difficult to assess whether the low

percentage of young observed in this study period is of concern. However, calf mortality is

high in the Bay of Islands (42% of calves die before reaching one year of age) compared with

other populations (Constantine, 2002; Tezanos-Pinto, 2009) and therefore recruitment levels

should be closely monitored. A small number of observed neonates or calves could also

indicate that mothers are actively avoiding the area. The proportion of juveniles was low

compared to 2007-2010 (8%, Hartel, 2010); however it fell within the range observed from

1997-2005 (2.1-10.9%). Errors can occur when classifying juveniles, especially when older

individuals are close to entering the adult population. In this study, there were a number of

individuals whose fins were unmarked, which is common for juveniles and young adults.

However, most were classified as adults based on their size (see table 3.1 for descriptions of

age classes). It is possible that the proportion of juveniles was therefore slightly

underestimated in this study. Conversely, it could reflect the low number of calves entering

into the juvenile population as a result of the high observed mortality rate.

3.5.2 Social structure

In the Bay of Islands, the observed HWI value was 0.27, which is slightly higher than the

typical values of 0.1-0.2 e.g. Panama City, Florida (Bouveroux & Mallefet, 2010), Galveston

Bay, Texas (Bräger et al., 1994), the outer southern Moray Firth (Eisfeld & Robinson, 2004),

and Shark Bay (Smolker et al., 1992). Low HWI values are indicative of populations that

exhibit a high degree of fission-fusion with dynamic grouping patterns. Factors such as

population size as well as connectivity with surrounding areas affect the frequency at which

individuals encounter each other and therefore the proportion of time they spend together (i.e.

HWI) (Bräger et al., 1994; Chilvers & Corkeron, 2002). For example, HWI are unusually

high in Doubtful Sound, New Zealand, and Sado Estuary, Portugal, (0.47 and 0.45,

66

respectively); both populations are small, and both are geographically and demographically

closed. This is in contrast to the Bay of Islands, which is only a part of this population’s

range where there is exchange from surrounding areas (Constantine, 2002). Abundance

estimates in the Bay of Islands ranged from 24-97 individuals for 2009-2012, which is low

compared to some populations where the HWI values are low e.g. Point Lookout,

Queensland (HWI=0.05, Chilvers & Corkeron, 2002; abundance = 700-1000 individuals,

Chilvers & Corkeron, 2003) and Panama City, Florida (HWI=0.10, Bouveroux & Mallefet,

2010; abundance = 58-177 individuals, Bouveroux & Mallefet, 2010). Moreover, due to the

singular group structure seen for bottlenose dolphins in the Bay of Islands on most days,

individuals have less opportunity to leave and join groups.

Association patterns are strongly influenced by the movement patterns of individuals for

bottlenose dolphins due to their highly social nature (Bräger et al., 1994; Quintana-Rizzo &

Wells, 2001). In populations that are not closed, there are often varying degrees of site

fidelity among individuals and association patterns may reflect these differences. In Cedar

Keys, Florida associations between dolphins that were sighted regularly and those sighted

sporadically were rare, with only 20% of sightings containing mixed groups (Quintana-Rizzo

& Wells, 2001). A similar pattern is seen in Sarasota Bay, Florida, where mixed groups of

residents and non-residents is rare (17%) (Wells et al., 1987). In this study, strong

associations mainly occurred between current core users or between occasional visitors (i.e.

former core users or individuals that have never been core users). This may reflect the

ranging patterns for individuals, where visitors are likely to maintain associations as they

travel away from the bay and then return together.

This study found that 30% of the population formed strong, temporally stable associations

within and between both sexes. While the permutation test for males was non-significant,

only six males were included in the analysis and therefore the ability to detect preferred or

avoided associations may have been compromised. Strong, long lasting associations between

individuals irrespective of sex is an unusual situation for bottlenose dolphins. While mixed-

sex groups are observed in other populations (Connor et al., 2000; Eisfeld & Robinson,

2004), associations are generally stronger and more stable within sex classes than between

and are thought to reflect sex-specific reproductive strategies (Connor et al., 2000). Female

associations are usually of moderate strength and stability, with females of the same

reproductive state usually grouping together, presumably because they require similar food

and protection (Connor et al., 2000; Rogers et al., 2004). Males are often found in pairs or

trios and form strong, long lasting alliances and cooperate to gain access to female mates

67

(Connor et al., 2000; Parsons et al., 2003; Wiszniewski et al., 2012). Finally, associations

between females and males are usually linked to the breeding season and are therefore

relatively brief (Connor et al., 2000). This, therefore, suggests that reproductive goals do not

play a major role in shaping associations in the Bay of Islands.

Gero et al. (2005) found bottlenose dolphins in Shark Bay, Australia, formed preferential

associations when foraging together, most likely as bi-product benefits result through

cooperation (Brown, 1983). They suggested that individuals with similar foraging tactics are

likely to be more successful when foraging together. Specialised foraging tactics, as well as

cooperative foraging, have been reported in a number of bottlenose dolphin populations

(Gazda et al., 2005; Rossbach, 1999; Sargeant et al., 2005; Smolker et al., 1997), and in some

cases are thought to be the predominant factor in distinguishing populations in the absence of

geographic barriers (Rossbach & Herzing, 1999). However, foraging techniques are not

specialised in the Bay of Islands, and cooperative foraging is rarely seen. It is possible,

however, that strong, long lasting associations within and between sexes are beneficial for

increasing foraging efficiency in a complex environment. In Doubtful Sound, New Zealand, a

small isolated population (56, CV=1.0%; Currey, 2007) of bottlenose dolphins is also

characterised by long lasting associations within and between sexes. This is thought to be in

part due to the nature of the fiord environment, which creates a patchy distribution of food

resources in time and space due to the low ecological efficiency (Matthews & Heimdal,

1979). The need for effective information transfer to locate important resources may be

strong in the Doubtful Sound community, especially due to its level of isolation, thus helping

to explain long lasting intra- and inter-sexual bonds. While the Bay of Islands is not isolated,

it is a large, convoluted area with varying depths and habitats (section 2.2.1). Dolphins within

the bay have a highly varied diet (Constantine & Baker, 1997; Hartel, 2010); however, some

areas of the bay are particularly rich in fish diversity, suggesting that food resources are

patchy in distribution (Brook & Carlin, 1992). As a result, mix-sexed groups in which

females and males form long lasting associations may in part be explained by the need for

increased prey detection and information transfer.

Anthropogenic activities can negatively impact the survival and reproductive success of

social marine mammals by disrupting association patterns, fragmenting core social units, and

eliminating key individuals (Wade et al., 2012). As a result, it is important to consider the

potential effects of human activities on vulnerable populations, such as coastal bottlenose

dolphins. For chimpanzees, abundance has a direct impact on the fission-fusion nature of the

community; strength of associations between sexes and temporal stability increases with

68

smaller community size (Lehmann & Boesch, 2004). A similar pattern was observed for a

small closed, philopatric community of bottlenose dolphins in the Sado Estuary, Portugal

(Augusto et al., 2012). Both the strength of the CoA as well as the temporal stability of

associations increased as community size declined, potentially in a response to intraspecific

competition between individuals (Gero et al., 2008). In the Bay of Islands, there has been a

marked decline in abundance, with now only a relatively small number of individuals using

the bay on a regular basis. While there has been an increase in the average strength of HWI

since it was last investigated (Mourão, 2006), there has been a decrease in the proportion of

long-lasting companionships by 16%. It is plausible that as abundance has decreased the

small number of individuals that use the bay regularly have strengthened their associations in

space. The decrease in the proportion of individuals that form long-term companionships

however is most likely due to the fact that many dolphins, notably those that were core users,

have either discontinued their use of the bay, or reduced their use of it significantly, which

has in turn fragmented social units.

The average time casual acquaintances spent together between 1996 and 2004 was six days

(Mourão, 2006) whereas it was less than one day in the current study. Repeated disruption by

vessels has the potential to induce short-term changes in behaviour and fission-fusion rates,

which has been observed in Port Stephens, Australia, as a direct result of dolphin-watching

activities (Allen, 2005; Steckenreuter et al., 2012). Higher rates of change in membership

were also found in a study in Shark Bay, Australia (Bejder et al., 2006a). This may also be

occurring in the bay; short-term responses to the presence of both dolphin-tour and private

boats may induce increased fission-fusion rates, and individuals that are more sensitive to this

sue the bay less. This may also be occurring in the bay; short-term responses to the presence

of both dolphin-tour and private boats may include increased fission-fusion rates, and

individuals that are more sensitive to this are using the bay less, resulting in fewer individuals

that use the bay frequently. An examination of environmental variables that may explain

changes in habitat use (Nathan, 2010) failed to find any conclusive explanation. Further

examination of more recent records should be undertaken to try and ascertain why this

population has declined which has no doubt affected their social structure.

3.5.3 Summary

In conclusion the social structure in the Bay of Islands is likely to reflect a combination of the

unique environment, residency patterns, a small population size and the impacts of tourism.

The bay only represents a small portion of the large range for the population, which helps to

69

explain the relatively fluid nature of the population. Unlike a number of other coastal

populations, reproductive goals do not appear to be the main force shaping their social

structure, which is evident from the temporal stability of associations both within and

between sex-classes. Instead, factors such as the need for efficient information transfer in a

complex environment may be more important for this population. It is unlikely that the social

structure of this population has been unaffected by anthropogenic activities, especially as

there has been a significant decline in abundance. A lower percentage of individuals were

found to form long lasting companionships in this study than previously reported, and this

decrease may have resulted as more individuals have permanently emigrate from the area, or

reduced their use of it, which has fragmented many social units. Moreover, the presence of

both private boats and dolphin tour-boats induce short term behavioural responses by

dolphins (Constantine, 2001; Constantine et al., 2004), perhaps increasing the rate at which

they enter and leave the bay. This may explain the shorter duration of time short-term

acquaintances spent together than previously seen.

70

4 General Discussion

4.1 Main Aims

The overarching aim of this thesis was to investigate the current population status of the

bottlenose dolphin population in the Bay of Islands and to describe their social structure.

More specifically, the objectives were to provide new estimates of abundance, apparent

survival, and temporary emigration; to provide a current census of the group dynamics; and

to assess the association patterns between dolphins both in space and time. This adds to a 19-

year project, and the on-going monitoring of this population has allowed for important

aspects of the populations ecology and biology to be assessed over time.

4.1.1 Abundance, survival and temporary emigration

Assessment of the current population status of bottlenose dolphins in the Bay of Islands

indicated that a relatively small number of dolphins are now seen in the Bay of Islands. The

abundance estimates were similar to those observed by Tezanos-Pinto et al. (2013) after there

had been a significant decline in population size. Whether or not the population is still in

decline was not determined; however, the results clearly show that the population is by no

means showing a sign of recovery.

Hartel (2010) showed that habitat use by dolphins in the Bay of Islands has changed in space,

and the results from this study indicate that habitat use has also changed in time. Temporary

emigration patterns of dolphins have changed from a random to Markovian nature, where the

animal ‘remembers’ that it is off the site and its return is based on some time dependent

function (Pine et al., 2003). While it appears that dolphins that use the bay regularly rarely

leave the site, visits by dolphins that utilise other parts of the population’s range more heavily

are infrequent. The overall change suggests that a smaller number of individuals are using the

bay on a regular basis, with a number of individuals only visiting the bay at specific times.

Determining why the temporal patterns of habitat use have changed is difficult due to the

complexity of the potential causal factors (Nathan, 2010). While effort by tourism boats is

higher in summer, the number of boats as well frequency at which they go out is varies based

on demand. Moreover, it is difficult to separate the effects of tourism from those associated

with private boat use within the Bay of Islands. There is a sharp increase in SST from

approximately December to May as a result of the East Auckland Current, bringing in a

71

diverse range of fish species (Brook & Carlin, 1992); inter-annual variation in factors such as

the exact time that the current comes through, and changes in fish distribution would make it

difficult to detect the influences of environmental factors on the time at which dolphins enter

the bay. Moreover, the ability to detect a trend may be further confounded due to an

interaction between boat traffic and annual changes in environmental conditions.

Nonetheless, it is plausible that the time at which dolphins visit the bay represents a trade-off

between habitat quality and the costs associated with boat traffic.

A study investigating the causal factors for the decline in abundance from 1997-2006

revealed that there was no relationship between the decline and calving rate, calf survival,

water temperature or stranding’s (Nathan, 2010). However, the number of individuals that

were classified as core users declined from 59 to 33 between the study periods 1997-1999

and 2003-2005. Hartel (2010) found that the number had further decreased to 30 individuals

in 2007-2010. An assessment of the re-sight rates was not done in the current study as the

study design restricted the sighting data to four months, which limited the ability to detect

true differences in site fidelity. However, it is noted that of the 30 core users from 2007-2010,

ten were never seen during this study, four were seen on two field trips, two were seen three

times, and 14 were seen on every field trip. This could indicate that there has been a further

reduction in the number of core users, and that a number of individuals have discontinued

their use of the bay, which is reflected by the low apparent survival rate (i.e high rate of

permanent emigration).

4.1.2 Group dynamics and social structure

Both group dynamics and social structure of bottlenose dolphins in the Bay of Islands have

changed since they were last investigated (Hartel, 2010; Mourão, 2006), which is likely to be

associated with the decline in abundance. The median group size was the highest recorded for

the population, groups containing calves were not significantly larger than those without, and

the population was composed mainly of adults. Larger group sizes may be due to the change

in habitat use, with dolphins now regularly seen in deeper waters, even though they are still

within the boundaries of the bay (Hartel, 2010). It is likely however that with a small number

of individuals using the bay more regularly, those that spend much of their time within the

area group together more readily, leading to larger group sizes. This could also in part

explain the lack of difference in group size between groups containing calves and those that

do not. However, the fact that the demographic structure is now comprised mainly of adults

may suggest that mothers could be actively avoiding the bay. However, it is important to note

72

that the number of surveys that were conducted in 2012 was small compared to previous

years, which may have affected the demographic results.

At a coarse scale, the social structure of bottlenose dolphins in the Bay of Islands has

remained the same since it was first investigated by Mourão (2006). Dolphins associate in a

non-random manner, without preference to sex and the population is characterised by two

levels of associations; short term acquaintances and long term companionships. Strong,

temporally stable associations between males and females is unusual for bottlenose dolphins,

however, it is also seen the small, isolated Doubful Sound population (Lusseau et al., 2003).

While the Bay of Islands population is very different from the Doubtful Sound population in

that there is exchange of individuals from surrounding areas, it is similar in that both areas

are large, complex environments and therefore information transfer may be of importance.

Moreover, the Bay of Islands population is now small, with fewer individuals visiting the

area, therefore limiting the number of associates an individual can have. There have,

however, been two notable changes at a finer scale. Casual acquaintances now generally only

associate for around one day, compared to the previous report of six days, and the proportion

of individuals that form long term companionships has decreased by 16%. It should be

considered that both short and long-term changes in social structure may be the result of

tourism pressures. Short-term behavioural responses in the form of increased fission-fusion

rates have been reported for bottlenose dolphins that are repeatedly disturbed by boats

(Bejder et al., 2006a; Wiszniewski et al., 2009). In Shark Bay, Western Australia, Bejder et

al. (2006b) found that tour-vessel activity displaced bottlenose dolphins to areas of low

impact when tourism levels increased. The fact that it appears that a number of dolphins

avoid the bay, and others have reduced their use of it, may explain the reduction in the

proportion of long-term companionships; social units have most likely been fragmented as a

result of the shift in habitat use by a number of dolphins. It should also be considered that the

dolphins now seen on a regular basis in the bay were born during the peak of the dolphin-

watch tourism activities (late 1990s through to the mid-2000s). It is therefore possible that

they are tolerant of, or habituated to, boat activities in the bay (Sini et al., 2005). What may

be of future concern are the costs on reproductive success (Bejder, 2005) on the dolphins that

remain, as stresses are not always apparent (i.e. a lag between effect and cause) and often

difficult to measure (Rolland et al., 2012).

4.2 Conservation and future research

The Bay of Islands has been identified as a critical part of the population’s range

(Constantine, 2002) and therefore the decline in abundance observed by Tezanos-Pinto et al.

73

(2013), the small abundance estimates found in this study, and evidence for a high rate of

permanent emigration are of concern.

A study by Tezanos-Pinto (2011) showed that 65% of individuals catalogued in the Hauraki

Gulf have also been documented in the Bay of Islands. This supported preliminary work done

by Berghan et al. (2008) and suggests that the Northland population is larger than first

anticipated, or that individuals with differing home range overlap in at least two preferred

areas. However, very little is known about the abundance of dolphins in other areas of

Northland population’s range. Moreover, no work has been conducted anywhere outside of

the Bay of Islands investigating habitat use by bottlenose dolphins or assessing the quality of

it.

Animals may actively avoid a site in response to disturbance because there is suitable habitat

nearby that they utilise (Gill et al., 2001), and in some cases, animals that are subject to

human disturbance may persist by relocating to suboptimal habitats (Naves et al., 2003). It

has traditionally been thought that when animals do not avoid disturbed areas that they are

robust to the potential impacts and therefore require little or no protection (Gill et al., 2001).

The ability to respond to disturbance by moving into habitats of lower quality may depend on

the condition of the animal. For example, those individuals that are in poorer condition may

have no choice but to stay within the area of disturbance to just to meet their energy

requirements (e.g. Beale & Monaghan, 2004). Therefore, it is possible that the dolphins that

still use the Bay of Islands on a regular basis may be at more risk than those that have shifted

their habitat use.

When considering the displacement of animals from critical habitat, it is important to

determine where they are going and understand how their discontinued use of the area will

affect the long term viability of the population. Due to environmental heterogeneity, a

population’s range usually incorporates variety of different habitats, some of which are

unable to support self-sustaining local demes (Howe et al., 1991). While the Northland

population range is large, it is isolated from both the Marlborough Sounds and Fiordland

population (Bräger & Schneider 1998; Constantine, 2002; Merriman et al., 2009; Tezanos-

Pinto, 2009). As with many riverine and coastal small cetaceans, this makes the population

vulnerable to the effects of anthropogenic activities (Reeves et al., 2003). Considering that a

large number of individuals appear to have permanently emigrated from the Bay of Islands, a

process that may still be occurring, it is critical that resources are invested into finding out

where the dolphins have relocated, determining the quality of the habitat and generating

74

estimates of population parameters (i.e. abundance, survival rates) in order to assess the long

term viability of the Northland population.

In reality, projects are often limited due to logistical and monetary constraints. Radio tracking

individual dolphins would provide valuable insight into individual movement patterns, and in

turn habitat use (e.g. Balmer et al., 2008). However, this is an expensive and logistically

challenging method. Alternatively, the use of multi-state models provides a more practical

option for investigating how individuals use their Northland range. Multi-state models can be

applied to data that are collected in multiple different states (Schwarz et al., 1993; Williams

et al., 2002). In this case, the different states are different geographic areas. As individuals

move from one discrete location to the next, a better description of the population will be

obtained via sampling in multiple locations (Nicholson et al., 2012).

The role of sociality in determining the effects of human impacts have been shown for

terrestrial species. For example, intensive hunting of African elephants has negatively

impacted the population through reduced fecundity and disrupted information transfer by

removing males who are the predominant breeders, and matriarchs who provide social

knowledge to less experienced individuals (Poole, 1987; McComb et al., 2001). In contrast to

terrestrial species, where more detailed observations of social behaviour can be made, it is

difficult to determine the exact social roles that dolphins have within their societies and

therefore determining what the consequences of losing particular individuals from a

population are is difficult. It appears that the displacement of a large number of bottlenose

dolphins from the Bay of Islands, possibly through commercial and private-boat activity, has

fragmented social units. Some individuals may act as brokers between communities, and the

cohesiveness between two communities may be jeopardised in their absence (Lusseau &

Newman, 2004). Social network analyses have not been conducted for the Bay of Islands

population and therefore it is not known whether there are brokers that act as a link between

those dolphins that use the bay regularly and those that utilise other areas of the Northland

range more regularly. The disruption of social bonds through the displacement of individuals

may have serious consequences for the viability of the population, especially if the likelihood

of an animal leaving the area of disturbance is based on individual tolerance levels (Bejder et

al., 2006b). Therefore future studies should focus on tracking the long-term movements of

individuals, determining the more sensitive age and sex-classes, and investigating the social

network for dolphins in the Bay of Islands to determine if there are animals that act as

brokers (Bejder et al., 2006b; Lusseau & Newman, 2004).

75

Anthropogenic sound has been identified as a potential threat to marine mammals.

Anthropogenic sound has the potential to affect the health of individuals by increasing stress

levels (Romano et al., 2004); disrupting communication; and impairing an animal’s ability to

learn about its environment or locate prey (Buckstaff, 2004; David, 2006). Avoidance

behaviour in response to motorised watercraft nose has been demonstrated for cetaceans (Au

& Perryman, 1982; Janik & Thompson, 1996; Nowacek et al., 2001) and therefore the high

level of boat activity in the Bay of Islands may be contributing the observed shift in habitat

use. The effect of anthropogenic noise on the behaviour of bottlenose dolphins in the Bay of

Islands therefore warrants investigation.

Given the economic importance of dolphin watch and swim-tourism in the Bay of Islands, it

is difficult to develop a solution that balances economic, social and conservation needs.

However, the decline of this population is not sustainable. Tourism pressure has been intense;

in 1997 there was an increase from 49 to 70 permitted trips per week, and due to this there is

substantial overlap between the tour boats, giving dolphins very little time to rest

(Constantine, 2002). Whist the pressure has eased slightly since 2012, dolphins are still

impacted due to harassment from private boats, and possibly from noise pollution generated

from boat activity (Buckstaff, 2004; Mattson et al., 2005; Van Parijs & Corkeron, 2001).

Given the current status of the population, it is suggested that all anthropogenic threats to this

population be re-evaluated. For long-lived, slow breeding mammals, the recovery of a

population is usually slow, which may be especially true for social cetaceans due to the

complexity of interactions between conspecifics (Wade et al., 2012). In the case of the Bay of

Islands, it appears that the population decline can in part be explained by permanent

migration. As a result, a faster recovery is more likely; other studies have found that dolphins

may return to an area when the disturbance is removed (e.g. Würsig et al., 2000). Given that

the Bay of Islands represents a critical part of the population’s range, it is possible that

dolphin numbers will increase once anthropogenic stressors are eased.

76

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Appendices

Appendix one:

Dolphin identification numbers, sex, date of first sighting, and total number of sightings for

each sighted in this study. Note: * indicates new dolphin, M = male, F = female, U =

unknown. A dolphin seen in association with a calf on at least two occasions was sexed as

female.

ID Sex First seen total sightings per animal ID Sex First seen total sightings per animal

5 U 8-Feb-12 2 543 U 1-Nov-12 2

9 U 8-Feb-12 8 544 U 1-Nov-12 2

55 M 1-Nov-12 4 *546 U 9-Feb-12 9

72 U 8-Feb-12 7 *549 U 11-Dec-12 1

79 F 1-Nov-12 2 *553 U 8-Feb-12 9

141 F 1-Nov-12 2 *554 U 8-Feb-12 6

153 M 8-Feb-12 8 *556 U 1-Nov-12 3

169 F 1-Nov-12 2 *557 U 8-Feb-12 4

170 U 1-Nov-12 2 *558 U 1-Nov-12 2

212 M 8-Feb-12 10 *559 U 1-Nov-12 2

218 U 1-Nov-12 2 *560 U 10-Jul-12 1

235 U 2-Nov-12 1 *561 U 2-Nov-12 3

325 F 2-Nov-12 1 *562 U 10-Feb 2

347 U 8-Feb-12 10 *563 U 12-Dec-12 1

382 U 2-Nov-12 1

406 U 11-Dec 1

421 U 1-Nov-12 2

424 U 2-Nov-12 1

471 F 10-Jul-12 5

475 F 8-Feb-12 9

483 U 2-Nov-12 1

494 F 8-Feb-12 6

495 U 8-Feb-12 10

499 U 8-Feb-12 9

503 U 1-Nov-12 2

507 U 1-Nov-12 2

509 U 8-Feb-12 10

510 F 8-Feb-12 10

511 U 8-Feb-12 9

513 U 10-Jul-12 3

514 U 8-Feb-12 10

515 U 8-Feb-12 9

516 U 8-Feb-12 7

517 U 8-Feb-12 9

519 U 8-Feb-12 6

522 U 10-Jul-12 7

525 U 8-Feb-12 8

526 U 12-Dec 1

527 U 10-Dec-12 2

531 U 1-Nov-12 2

533 U 11-Dec-12 1

541 U 1-Nov-12 2

98

Appendix two:

Full list of RD models fitted to the capture histories of bottlenose dolphins the the Bay of Islands to

estimate parameters for population size, survival, emigration and capture probability, which were

allowed to vary with time both within and between sessions. Phi=apparent survival; g”=probability of

temporary emigrating off the study site; g’=probability of remaining a temporary emigrant

p=probability of capture. Where (.) = constant between sessions; (s) = varies between primary sessions

and; (t) = varies within sessions. Markovian temporary emigration = g”,g’; random temporary

emigration = g and; no g parameter is found in no movement models. In all models, recapture

probabilities were set to equal capture probabilities c=p. Highlighted models were deleted as a number of

parameters were either not being estimated, or large 95% confident intervals.

Model QAICc ΔQAICc AICc Weights Num. Par

phi(.) g"(s) g'(.) c=p p(s*t) 101.9056 0 0.90901 44

phi(.) g"(s) g'(s) c=p p(s*t) 108.1863 6.2807 0.03933 49

phi(.) g"(.) g'(.) c=p p(s*t) 109.5665 7.6609 0.01973 39

phi(.) g"(.) g'(t) c=p p(s*t) 109.9559 8.0503 0.01624 43

phi(s) g"(.) g'(.) c=p p(s*t) 111.1276 9.222 0.00904 45

phi(.) y(.) c=p p(s*t) 112.105 10.1994 0.00554 37

phi(.) y(s) c=p p(s*t) 115.5298 13.6242 0.001 45

phi(s) g"(s) g'(s) c=p p(s*t) 120.3344 18.4288 0.00009 54

phi(s) y(s) c=p p(s*t) 122.4653 20.5597 0.00003 51

phi(.) c=p p(s*t) 148.1584 46.2528 0 38

phi(s) c=p p(s*t) 151.9146 50.009 0 44

phi(.) g"(s) g'(.) c=p p(s) 166.1114 64.2058 0 23

99

Appendix three:

Parameter estimates from the best fitting (constant survival, constant temporary emigration

and full time varying capture probabilities with c=p) model with heterogeneity. Many of the

parameters were not estimated. Both survival (index 1) and abundance estimates (index 70-

77) were similar to those from the best fitting model suggesting that did not incorporate

heterogeneity in capture probabilities.

Index Label Estimate SE 95% LCI 95% UCI Index Label Estimate SE 95% LCI 95% UCI

1 S 0.636824 0.0376825 0.5602706 0.7070189 50 p Session 50.753885 0.248822 0.181084 0.976976

2 Gamma'' 0.136153 0.0328376 0.0835667 0.2140984 51 p Session 50.115601 0.228572 0.001631 0.912713

3 Gamma' 0.497734 0.1262463 0.2691668 0.7272529 52 p Session 6 0 1E-07 -2E-07 2E-07

4 pi Session 1 0.566667 0.090472 0.3884399 0.7291675 53 p Session 60.163211 0.185339 0.013461 0.736008

5 pi Session 2 0.160774 0.2166314 0.0081676 0.8167408 54 p Session 60.139689 0.192113 0.007026 0.788412

6 pi Session 3 0.653527 0.1385411 0.3624726 0.8622147 55 p Session 60.947112 0.066259 0.572635 0.995839

7 pi Session 4 0.513206 0.5129036 0.0185032 0.9833214 56 p Session 60.833334 0.087841 0.591417 0.94527

8 pi Session 5 0.712215 0.1993311 0.2689491 0.9433367 57 p Session 6 1 1E-07 1 1

9 pi Session 6 0.272586 0.1427104 0.0837685 0.6056665 58 p Session 7 1 9.1E-06 0.999982 1.000018

10 pi Session 7 0.227273 0.0893463 0.0978873 0.443584 59 p Session 7 0 0.000869 0 1

11 pi Session 8 0.28125 0.0794804 0.1533065 0.458188 60 p Session 70.400001 0.219089 0.100229 0.799595

12 p Session 1 1 0.0000005 0.9999991 1.0000009 61 p Session 70.764706 0.102879 0.514491 0.908821

13 p Session 1 0.352941 0.1159041 0.1678594 0.595946 62 p Session 7 1 2.8E-06 0.999995 1.000006

14 p Session 1 0.470588 0.1210578 0.2553704 0.6973279 63 p Session 70.764706 0.102879 0.514491 0.908822

15 p Session 1 0 0.0000001 -2E-07 0.0000002 64 p Session 80.222222 0.13858 0.056027 0.579016

16 p Session 1 1 0.0000219 0.9999571 1.0000429 65 p Session 8 0 1.9E-06 -3.7E-06 3.7E-06

17 p Session 1 0 0.0000002 -4E-07 0.0000004 66 p Session 8 1 1E-07 1 1

18 p Session 2 0.321892 0.4250926 0.0103316 0.9557222 67 p Session 8 0 1E-07 -2E-07 2E-07

19 p Session 2 0.674242 0.4232416 0.0452412 0.9890599 68 p Session 8 1 1E-07 1 1

20 p Session 2 1 0.0000263 0.9999485 1.0000515 69 p Session 80.652174 0.099311 0.442882 0.815581

21 p Session 2 0.659417 0.1195864 0.405415 0.8461017 70 N Session 1 30 4.14E-05 30 30.00005

22 p Session 2 0.344044 0.0927919 0.189814 0.5400559 71 N Session 251.09815 5.159701 45.9803 69.44253

23 p Session 2 0.214035 0.2038717 0.0246961 0.7454636 72 N Session 359.61656 4.030309 54.87628 72.1691

24 p Session 3 0 0 0 0 73 N Session 464.57881 5.796384 58.20505 83.62781

25 p Session 3 0.21287 0.0796035 0.0963132 0.4069606 74 N Session 581.22362 4.271495 76.84164 96.03208

26 p Session 3 0.428036 0.102452 0.2478553 0.6295636 75 N Session 625.61734 5.462613 21.73791 49.89203

27 p Session 3 0.319305 0.1000473 0.1598739 0.5362449 76 N Session 7 22 0 22 22

28 p Session 3 0.434199 0.1062248 0.2474572 0.6416978 77 N Session 8 32 0 32 32

29 p Session 3 0.702663 0.2658979 0.1632231 0.9662507

30 p Session 3 0 0.0000003 -5E-07 0.0000005

31 p Session 3 0.196431 0.1010352 0.0651722 0.461531

32 p Session 3 0 0.0000002 -5E-07 0.0000005

33 p Session 3 0.736899 0.1136545 0.4702683 0.8983375

34 p Session 4 0.210222 0.2248371 0.0183821 0.7909489

35 p Session 4 0.149048 0.1094726 0.031255 0.4874127

36 p Session 4 0.558678 0.1465679 0.2830586 0.8023328

37 p Session 4 0 0.0000009 -1.7E-06 0.0000017

38 p Session 4 0 0 0 0

39 p Session 4 0.064494 0.0441198 0.0161784 0.2242166

40 p Session 4 0.677458 0.0839666 0.4972352 0.8168699

41 p Session 4 0.981497 1.0074363 0 1

42 p Session 5 0.225105 0.0641917 0.1237436 0.3740512

43 p Session 5 0.680551 0.1870098 0.282986 0.9199969

44 p Session 5 0.2239 0.062323 0.1249852 0.3681599

45 p Session 5 0.393379 0.0794125 0.2524884 0.5545623

46 p Session 5 0.51169 0.0791932 0.3602114 0.6610511

47 p Session 5 0.263434 0.1201653 0.0960408 0.5462713

48 p Session 5 0 0.0000002 -4E-07 0.0000004

49 p Session 5 0.093673 0.1182246 0.0066996 0.6129659

100

Appendix four:

Description of the photo-identification catalogue of the bottlenose dolphins in the Bay of

Islands.

The photo-identification catalogue is a collection of photographs of identified dolphins from

the Bay of Islands. It includes the best photos of the left and/or right sides of the dorsal fin of

496 dolphins. It has been maintained at the University of Auckland since 1994; fin

photographs have been updated and new individuals have been added over the years. The

catalogue exists in both digital and paper form, both housed at the University of Auckland

under the care of Rochelle Constantine. A digital copy is included with this thesis. Access to

the catalogue will be considered upon request to the curator, Rochelle Constantine

(r.constantine@auckland.ac.nz) who will liase with the other contributors as appropriate.

Terms of Use

As this is part of an ongoing research project, numerous people have contributed to the

catalogue. It is acknowledged that Rochelle Constantine has been the main contributor and

has research oversight for the projects undertaken. Olivia Hamilton made a contribution to

the current version of this catalogue, including updating photos of known individuals plus the

addition of 17 new dolphins. The terms of use are outlined in both Constantine (2002) and

Tezanos-Pinto (2009). Furthermore, requests for use of the catalogue for scientific or

educational purposes will require the written permission of Olivia Hamilton. Olivia Hamilton

reserves the right to be included as co-author on scientific publications or reports that have

resulted from the use of the catalogue (either digital or paper version) or any other data from

2012.