ohamilton MSc 2013

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
1.6.3 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
3.5.3 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

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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

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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

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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

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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.

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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.

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