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Using GIS to identify potential dynamic marine protected areas: a case study using shortfin mako shark tagging data in New Zealand

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Using GIS to identify potential dynamic marine protected areas:
A case study using shortfin mako shark tagging data in New Zealand

by

Marie Anne Taylor

A Thesis Presented to the
Faculty of the USC Graduate School
University of Southern California
In Partial Fulfillment of the
Requirements for the Degree
Master of Science
(Geographic Information Science and Technology)

December 2019

Copyright © 2019 by Marie Taylor

To my parents, for supporting me in every adventure I undertake

iv
Table of Contents
List of Figures ................................................................................................................................. vi
List of Tables ................................................................................................................................ vii
Acknowledgements ..................................................................................................................... viii
List of Abbreviations ...................................................................................................................... ix
Abstract ............................................................................................................................................ x
Chapter 1 Introduction: Why Sharks? ........................................................................................... 11
1.1. Location: New Zealand .................................................................................................... 11
1.2. Motivation ........................................................................................................................ 15
1.3. Protecting Shortfin Mako Sharks in New Zealand ........................................................... 16
1.3.1. New Zealand Culture Significance .......................................................................... 18
1.3.2. Community Involvement and Enforcement ............................................................ 18
1.4. Research Goals ................................................................................................................. 19
1.5. Contents of This Document .............................................................................................. 19
Chapter 2 Related Research ........................................................................................................... 21
2.1. Different Species, Same Goal: The Use of Satellite Tags ................................................ 21
2.2. Shark Behavior and Ecology ............................................................................................ 24
2.3. New Zealand’s Current MPA Policies ............................................................................. 26
2.4. Understanding Our Role in Protection ............................................................................. 28
2.4.1. Existing Work in MPAs .......................................................................................... 29
2.4.2. MPAs vs Dynamic MPAs ....................................................................................... 29
2.5. GIS in Marine Conservation ............................................................................................. 30
2.6. Summary ........................................................................................................................... 31
Chapter 3 Data and Methods ......................................................................................................... 33
3.1. Mako Shark Data .............................................................................................................. 33
v
3.1.1. Data Source ............................................................................................................. 33
3.1.2. Tagging Data Exploration ....................................................................................... 36
3.1.3. Seasonal Water Temperature Changes .................................................................... 40
3.2. Data Preparation ............................................................................................................... 41
3.3. Methodology to Identify Core Areas for DMPAs ............................................................ 42
3.3.1. The “Spaghetti and Meatballs” Method .................................................................. 42
3.3.2. Identification of Core Areas for DMPAs ................................................................ 44
3.4. Summary ........................................................................................................................... 47
Chapter 4 Results ........................................................................................................................... 48
4.1. Workflow Results ............................................................................................................. 48
4.1.1. Creating Track Lines & Seasonal Splits .................................................................. 48
4.1.2. Buffered Polygons and Spaghetti ............................................................................ 50
4.1.3. Meatball Spatial Join ............................................................................................... 52
4.1.4. Selecting and Symbolizing Join Count .................................................................... 54
4.2. Final Result ....................................................................................................................... 56
4.3. Analysis of Results ........................................................................................................... 58
Chapter 5 End Results: What Does It Mean? ................................................................................ 61
5.1.1. Changes and Improvements .................................................................................... 61
5.1.2. Future Work and Applications ................................................................................ 62
References ..................................................................................................................................... 65

vi
List of Figures
Figure 1. New Zealand Reference ................................................................................................. 12
Figure 2. New Zealand Marine Environments .............................................................................. 13
Figure 3. Ocean Depth Ranges in New Zealand ........................................................................... 14
Figure 4. SPOT Tag Example. Source: MarineCSI 2010 ............................................................. 23
Figure 5. Mako Shark Anatomy. Source: Bigelow and Schroeder 1948 ...................................... 26
Figure 6. Tuna Anatomy. Source: Bigelow and Schroeder 1948 .................................................. 26
Figure 7. Kernel Utilization Distribution (KUD) Models. Source: Francis 2018 ......................... 36
Figure 8. Mako Data - Full Extent ................................................................................................. 37
Figure 9. ModelBuilder Workflow ................................................................................................ 44
Figure 10. Project Workflow ......................................................................................................... 46
Figure 11. Individual Mako Sharks ............................................................................................... 49
Figure 12. Data with Track Lines .................................................................................................. 50
Figure 13. Buffered Seasonal Tracks ............................................................................................ 51
Figure 14. "Meatball" Centroid Points .......................................................................................... 52
Figure 15. Spaghetti and Meatballs ............................................................................................... 53
Figure 16. Union of Spaghetti and Meatballs ................................................................................ 54
Figure 17. Winter Join Count Areas .............................................................................................. 55
Figure 18. High Use Areas for Winter .......................................................................................... 56
Figure 19. Seasonal High Use Areas ............................................................................................. 57
Figure 20. Fall Tuna Overlap ........................................................................................................ 59
Figure 21. Spring Tuna Overlap .................................................................................................... 60

vii
List of Tables
Table 1. Proposed New Zealand New MPA Designations ............................................................ 27
Table 2. Tagging Data Quality Details. Source: Francis 2018 ...................................................... 35
Table 3. Tagged Shark Details ...................................................................................................... 38
Table 4. Tagging Data Example .................................................................................................... 38
Table 5. New Zealand Season Breakdown .................................................................................... 39
Table 6. Detailed Workflow Outline ............................................................................................. 47

viii
Acknowledgements
I am grateful to my family and my partner, who constantly support me in everything I do, and
who have pushed me through the hard times that are inevitable in graduate school. I thank all of
the professors and staff at USC, for guiding me through this program and assisting me when I
needed help both within the classroom and with IT help. Thank you to my committee members,
Dr. Lee and Dr. Loyola, for providing advice when needed and helping to guide me in the last
stages of my final semester. Specifically, thank you to Dr. Laura Loyola, who helped me in all
the stages of this thesis work, start to finish, as well as through classes. Our shared interests in
anthropology, biology, as well as the environment helped me to see the realistic applications for
GIS in my future. Special thanks to Dr. Lee, who provided such an outlet for me to investigate
the possibilities of remote sensing in marine research, through his class. To all of my classmates
who have been such assets, specifically Ashley Reade and Lindsay Hennes, who helped me in
my times of need, thank you. A colossal thank you to Dr. Malcolm Francis and his team at
NIWA, the National Institute of Water and Atmospheric Research, in New Zealand for providing
both guidance as well as data. This truly couldn’t have happened without you and I am forever
grateful. To Dr. Sedano, whose supervision I could not have done without in the thesis
development semester. And finally, to my advisor Dr. Karen Kemp, who has been so helpful in
this entire endeavor, giving both time and energy to motivate and assist me at every step. I am
fortunate to have had such a wonderful support system, both personally and through USC.

ix
List of Abbreviations
CHUAs Core Habitat Use Areas
DMPAs Dynamic Marine Protected Areas
EEZs Exclusive Economic Zones
GIS Geographic Information System
MPAs Marine Protected Areas
NIWA National Institute of Water and Atmospheric Research
NOAA National Oceanic and Atmospheric Association
SPOT Smart Position and Temperature (Transmitting Tags)
SSSM Switching State Space Model
UTM Universal Transverse Mercator

x
Abstract
Analyzing pelagic shark behavior is an ongoing challenge due to the highly migratory nature of
these animals, as well as outside threats such as overfishing and climate change. Increased
protection of vital habitats is essential in combating declining species numbers. Although some
shark species, like the shortfin mako (Isurus oxyrinchus), have made a steady comeback in the
last decade, there is still significant room for improvement. Comprehending the connection
between how sharks use their environment and move between protected territories can benefit
our understanding of shark behavior and conservation as a whole. By analyzing shark
movements over time and creating visual representations of core habitat use areas, an assessment
can be made on the potential for implementation of seasonal dynamic marine protected areas
(DMPAs) in New Zealand’s waters to aid in pelagic conservation.
Starting with a large spatio-temporal dataset of tagging data collected for 13 mako sharks
over five years, these data points were first cleaned and filtered in order to create individual
shark track lines for visualization of the data as a whole. Next each shark’s track was divided
into seasonal chunks and these were buffered to a 32km wide zone, which, based on the data,
accounts for an average day’s movement of a mako shark. This collection of seasonally tagged
polygons represent the areas used by each shark in each season. The next step was to intersect
and count overlapping seasonal polygons to identify the “high use” areas. The result is a map
showing areas where seasonal closures might benefit overall conservation, the areas to consider
as the core for future DMPAs.
11
Chapter 1 Introduction: Why Sharks?
Sharks have been a subject of discussion spanning a multitude of disciplines from folklore to
scientific research. Rising ocean temperatures and overfishing cause detrimental effects on the
ocean’s ecosystem (Chin et al. 2009). Marine biologists around the globe are working to develop
new methods of tracking and monitoring pelagic sharks, like the shortfin mako shark (Isurus
oxyrinchus, henceforth referred to as ‘mako sharks’), so that we can better understand their food
web and how they utilize their environments. Understanding this delicate ecosystem will aid in
increased local and global conservation efforts as well as improving upon conservation efforts
already in place. This project spatially analyzed the core habitat use areas (CHUAs) for the mako
sharks in New Zealand’s territorial waters and created a methodology that can be used to
delineate seasonal dynamic marine protected areas (DMPAs) for any species.
1.1. Location: New Zealand
Using data obtained from Dr. Malcolm Francis of the National Institute of Water and
Atmospheric Research (NIWA), this study is focused on the waters around the northern island of
New Zealand as this is where his thirteen tagged mako sharks spent the majority of their time.
The exclusive economic zones (EEZs) and marine protected areas (MPAs) already in place
protect mainly static species surrounding islands, shorelines, and inlets. By analyzing the mako
shark’s habitat use in regard to these specific protected areas, this project may help to expand
conservation efforts by illuminating areas for improved protection for pelagic species.
Choosing to focus this research on the mako sharks tagged off of New Zealand coastline
was a result of accessible data as well as an interest in helping to improve the current marine
regulations in place. There are 14,882 marine protected areas or reserves globally, which only
covers 7.59% of the ocean (UNEP-WCMC and IUCN 2019). New Zealand hosts 107 MPAs,
12
with over 30 of those areas designated as no-take reserves. This totals over 12,000 sq. km,
comprised of the Auckland Islands Marine Reserve near the southern island and the Kermadec
Islands Marine Reserve to the north. Despite the large number of reserves, there are still
significant challenges that New Zealand faces while aiming for a 10% marine protected
environment goal (MCI 2019). Figure 1 gives reference to the north and south islands
comprising New Zealand and its location in relation to Australia.

Figure 1. New Zealand Reference
Figure 2 shows the EEZs outlined as well as the current marine reserves surrounding the
islands. Most of the marine reserves are relatively small and are within inlets, close to the
shoreline, though there are significant reserves off the mainland.
13

Figure 2. New Zealand Marine Environments
The islands and the neighboring areas provide a unique hunting ground for the apex
predators that reside in the waters off the coast of New Zealand, specifically mako sharks. The
surrounding plateaus give increased protection from deeper waters for all the local wildlife, and
the varying depth changes provide exclusive hunting and migratory opportunities for the ocean’s
larger predators. Figure 3 shows the varying depth ranges off the coastline and surrounding
waters of New Zealand.
14

Figure 3. Ocean Depth Ranges in New Zealand
The significant variations in seabed topography supports a wide array of wildlife for this
area. This combination of flat plateaus and shelves, with large trenches and deep waters, makes
this location particularly interesting to larger pelagic shark species like the mako. Understanding
that the various depth ranges that surround New Zealand are favored by sharks when they hunt
helps to comprehensively understand how these marine animals move in and use their
environment. Observing connections between seafloor topography and favored core habitat areas
where these mako sharks reside can aid in understanding the gaps in marine protection
regulations and where improvements could be made.
15
1.2. Motivation
There is no greater need for marine conservation as there is right now. Every piece of
understanding helps in the overall goal of conservation. This project uses the tagging data from
mako sharks in New Zealand to help create a generalized methodology that can be used to
delineate areas suitable for seasonal closures to be used in an effective DMPA. The shark tagging
data includes a latitude and longitude location, year, time, tag number, sex of the shark, a
nickname given to each individual, along with other pertinent information. This project used GIS
techniques to delineate the zones in New Zealand’s waters that might be identified as dynamic
protected areas for these mako sharks. This is all in an attempt to benefit biological and
environmental conservation not only for the New Zealand government, but that can be applied to
other areas around the world.
By acknowledging areas as CHUAs, stronger restrictions and management decisions can
be made to ensure species and environmental survival. Seasonal closures or catch restrictions in
areas of high usage may help bolster species numbers throughout the year, combating some
population decline caused by climate changes. The relationship between climate change and
animal behavior adaptation is becoming more prevalent and understanding how species use these
habitats that are under siege from climate change, overfishing, or resource overuse could prove
useful in numerous ways (Rosa 2014).
There are a number of studies that show site-fidelity among larger pelagic sharks, hinting
that they can also remain within known areas for extended periods of time (Francis et al. 2018).
This could mean that the previous regulations covering the existing marine protected areas
(MPAs) may not be effective in protecting mako sharks. Without adjusting the definition of
marine protected areas to include mobile pelagic sharks, moving both inshore and offshore, we
16
are creating a vulnerability that could be exploited by commercial and recreational fishing.
Creating dynamic marine protected areas that can change over seasons could help cover a larger
area and provide greater protection for pelagic species.
Being one of the most threatened groups of marine animals globally has planted sharks at
the forefront of conservation concerns. Overfishing along with habitat loss has caused several
species to change and adapt. However, at the rate of current degradation it is impossible for
sharks to adapt as rapidly as necessary (Rosa et al. 2014). The issue is global, not local, but the
more we can understand at a local level, the better we can be at implementing real global change.
At a local level, Rosa et al. were able to make a direct link between temperature and pH changes
and the behavior of juvenile sharks in tropical waters. Their work emphasized experimental-
based risk assessments connecting sharks to climate change, and the necessity of this exploration
to aid policy-makers in protecting endangered species.
The effectiveness of MPAs as a tool to conserve large pelagic sharks is currently being
examined based on the migratory nature of large apex predators. In one study, scientists
attempted to understand the relationship that tiger sharks (Galeocerdo cuvier) have with their
food source, in this case green sea turtles (Chelonia mydas), and uncovered a direct spatial
pattern that can be observed between the two species (Acuña-Marrero et al. 2017). By using
spatial analysis, there is an advantage to discovering spatial relationships and how specific
species interact, which can benefit ecologists and biologists alike.
1.3. Protecting Shortfin Mako Sharks in New Zealand
Shortfin mako sharks are among several shark species that reside primarily in open ocean
ecosystems. They are known to travel great distances, reach up to at least 545 kg (1200 lbs.), and
can reach cruising speeds of 74 kilometers per hour and even 100km/hour for short bursts, which
17
can help catch their favored fast prey, tuna (Oceana 2019). These predators reside in tropical and
temperate locations worldwide which means they are targeted commercially for fishing as well
as included in accidental bycatch.
New Zealand’s waters play host to a variety of large marine predators due to the
moderate temperatures. Great whites (Carcharodon carcharias) and tiger sharks (Galeocerdo
cuvier) are known to frequent the territorial waters around New Zealand and Australia. Mako
sharks have been categorized as largely pelagic creatures that, along with great whites, make
long-distance movements. Considered as both coastal and oceanic, their habitat spans waters
from 0-600m in depth and 16ºC or warmer, though it is known this species has made dives to
deeper waters as cold as 10ºC. There is data to show that mako sharks tagged in New Zealand
have traveled as far as Fiji, Tonga, and New Caledonia. One shark travelled over 13,000km in
only 6 months, moving back and forth between New Zealand and Fiji (Ebert, Fowler, and
Compagno 2013).
Because these sharks can travel such vast distances in their lifetime means they are
susceptible to oceanic fishing tactics. Mako shark numbers have historically dwindled due to
longline fishing tactics that target marlin, swordfish, and tuna, which inevitably attract pelagic
sharks to be caught either on purpose or accidentally. Makos are valued highly for their fins and
meat, which are of high quality because, like tuna, they share a countercurrent exchanger blood
vessel structure. Other species that have this adaptation, like tuna and great white sharks, have
the ability to maintain their body temperature despite the surrounding waters. Adaptations paired
with specific evolutionary changes makes mako sharks both ideal pelagic hunters and viable
product for meat markets worldwide.
18
1.3.1. New Zealand Culture Significance
Island cultures worldwide have a strong tie to the ocean as not only part of their food
source, but in mythology and tradition as well. The Māori of New Zealand are no different and
they have relied heavily on fishing for their survival. However, as true with many other island
cultures, Māori people believe strongly in the concept of conservation and they have a legitimate
concern in regard to outsiders encroaching on their land and cultural beliefs (Roberts et al. 1995).
This is of the utmost importance when discussing how to implement new conservation methods.
Community involvement is the only way to successfully and sustainably maintain a network of
marine protected areas.
1.3.2. Community Involvement and Enforcement
One topic that cannot be overlooked for a successful protected area is a clear and
achievable management plan. Pelagic ocean management is difficult, so having feasible goals
makes it easier for local and state governments to be involved and to track progress. Making this
plan not only cover management goals but community participation means that the project will
engage people rather than exclude. Again, this involvement is essential for any long-term goals
to be achieved. Creating a conversation about local customs and cultural sensitivities means that
policy makers will not be overlooking a group of people that rely heavily on fishing.
Specifically, pelagic species can cover a vast amount of space in a lifetime. This means
that enforcement is nearly impossible for all life stages of a particular shark. We can focus on
areas where consistency is known, like breeding or birthing areas, but once the shark leaves these
zones they are in open water. Using the local fisherman communities is a way to help enforce the
restrictions without using an exhaustible amount of law enforcement. Creating incentives for
fishermen to be held accountable for their catch means that they are far more likely to engage in
19
reporting true catch numbers as well as conservation efforts. Using GIS techniques to outline
areas that are viable for fishing and areas that it could be detrimental to the ecosystem, and
having this information available to the public, is instrumental for success.
1.4. Research Goals
The main goal of this project is simple: to create a workflow that can be applied to any
marine species tracking data in any location. Specific objectives were to use the mako shark
tracking data to determine migratory patterns that can yield overlapping areas of use. From there,
these overlapping areas could be used to create CHUAs which indicate the need for increased
protection in these particular areas. Seasonal breaks can thus be created to ensure that
commercial or economic resources are not eliminated. This can provide clear and malleable areas
that could be opened and closed per seasonal fluctuation based on species use.
Using the tagging data from the mako sharks, a generalized workflow was created, and
high use areas were identified. This workflow resulted in the identification of overlapping areas
where the tagged mako sharks congregated during various seasons. While this project was not
intended to suggest specific management actions, it did demonstrate how dynamic protection
areas can be identified using available data and various GIS techniques. These results can be
used directly by the local government when they consider seasonal closures and catch limits.
1.5. Contents of This Document
Following this introduction there are four additional chapters. Related research is
discussed in the next chapter. From this, the reader will gain an understanding of the tactics used
in satellite tagging, the existing work being done with MPAs and conservation as a whole, and
the issues that come along with global climate change. Chapter Three covers the data compiled
and the preparation of that data used throughout the project. In addition to the data discussion,
20
this chapter discusses the requirements and constraints of developing a DMPA, including the
necessity for community involvement and cultural respect, as well as the methodology developed
to identify seasonal high use areas and outlines the steps to achieve the same result using
different data.
Chapter Four discusses the results from the methodology outlined in Chapter Three.
Using programs like ArcGIS Pro and ModelBuilder, a clear workflow was created that can be
explained and applied to any other project if needed. Chapter Five includes a summary of the
results as well as an analysis of steps taken, process failures encountered, successes and provides
an assessment of the results and future applications for this project.

21
Chapter 2 Related Research
By studying different species, specifically marine animals, we may be able to understand how to
properly implement and maintain protected areas to ensure the preservation of rapidly declining
ecosystems. Reviewing previous research that focused on pelagic shark behavior, marine
resource management, and specific marine ecologies aided in understanding the overarching
topics addressed in this thesis.
2.1. Different Species, Same Goal: The Use of Satellite Tags
Satellite tagging has been a crucial part of studying the behaviors of migratory animals,
particularly helpful with marine animals. By using electronic tags, rather than traditional number
marked tags, scientists are provided with much more detail on pelagic shark behavior. These tags
have improved scientists’ knowledge concerning life history, migration, and general behavior of
sharks (Ebert, Fowler, and Compagno 2013). According to Ebert et al., satellite tag development
has continued over the years to include various types of tags for different situations, most
popular being satellite and acoustic tags. Importantly, these methods can be applied to tracking
almost any marine animal.
Using spatiotemporal techniques to understand the distribution of tagging locations of sea
turtle nesting sites along the east coast of Florida, scientists can explore how human and climate
changes can cause shifts in nesting locations. Ecological spatial pattern hypotheses are usually
linked to the dispersal of resources or other critical features (Weishampel et al. 2003). By
understanding the change in nesting locations and the decrease in the accuracy of individuals
finding their way back to the same beaches, scientists can understand how humans and the
environment affect change in animal behaviors.
22
Studying sea turtles is significantly easier than a pelagic species of shark because sea
turtles have shown extreme site fidelity, by coming back to the same beach where they were born
to give birth, as well as having the necessity to leave the water to lay their eggs. This gives
conservationists time and opportunities to implant tracking devices and monitor nests and
hatchlings easily. Luring sharks into a location where a team can use tracking equipment is
strenuous and can be incredibly dangerous; however, the amount of data received could
potentially be worth the risk.
Spatial data is necessary to understand movement in such a vast area as the ocean.
Satellite tags are invaluable to teams attempting to create a full picture of the life cycle of a
pelagic shark. Creating areas of conservation, either protected areas or reserves, requires in-depth
knowledge of how an animal moves through and uses their environment. Some sharks can travel
several miles a day, or move seasonally over great distances, while other species are in-shore
most of their life. The ability to combine all of these data points with habitat and protection
requirements to come up with areas that could be used in conservation is essential.
One study used tracking devices affixed to the shells of sea turtles to understand how
better to protect one of the most endangered species, the Olive Ridley sea turtle, in Central
Africa (Maxwell et al. 2011). By using satellite data and telemetry, a collection process for data
collected in remote or inaccessible locations, these researchers were able to observe animal
biology and movement and how they relate to political boundaries. Inevitably, this can lend
information on how to approach conservation jurisdiction and effectiveness. In this case, the
concern was centered around the effectiveness of the MPAs surrounding Gabon and studying
how the error that comes with satellite telemetry can cause ineffective boundaries. Ultimately,
the goal of the project was to understand the distribution of a particular species of animal within
23
an already established boundary, the MPA. The results found that there is a significance to the
area when combined with other sea turtle tracking data from different species, thus proving the
need for increased and maintained protection off the coast of Gabon.
Tracking sea turtles by satellite systems, in many cases the Argos satellite system, and
then downloading the information automatically gives researchers an edge in understanding
animal migration and behavior. In addition, this can provide information needed to move
forward with addressing effective conservation methods. Smart Position and Temperature
(SPOT) transmitting tags are used on the mako sharks within this project but are increasingly
used throughout marine research. Unlike previous archival tags that stored information and had
to be retrieved, SPOT tags are equipped with a strong radio transmitter that can relay information
back to satellites as far as 1000km above the Earth (Ocean Tracks 2017). Figure 4 shows an
example of a SPOT tag, though they can come in several shapes and sizes depending on the
target species. Useful in marine mammals that breathe air, these tags have also had great success
when affixed to the dorsal fins of shark species as they ascend to the surface to feed.

Figure 4. SPOT Tag Example. Source: MarineCSI 2010
A project using remote sensing and satellite tracking data monitored pelagic sharks as
they moved through the North Atlantic Ocean and their relation to fishing fleets that patrolled the
24
same areas. The team of scientists focused on this particular area because of it being one of the
most heavily fished ocean ecosystems, both sharks and humans following the fish (Queiroz et al.
2016). By tagging and tracking over 100 sharks over approximately 8,000 days they were able to
understand the spatial distribution between the shark migration patterns and the two fishing
fleets. By identifying the locations where the animals and vessels overlapped, they were then
able to focus on those areas for conservation.
2.2. Shark Behavior and Ecology
In general, sharks are highly migratory animals which presents a distinctive challenge for
conservationists due to their ranges being so vast. These migratory sharks, along with tuna, sea
turtles, and cetaceans, are often victims of pelagic or bottom longline fishing (Calich, Estevanez,
and Hammerschlag 2018). Fishing in remote areas presents a difficult challenge when it comes
to conservation as longline fishing specifically targets large pelagic species, including tuna,
marlin, and sharks. Adding fuel to the fire, overfishing remains mostly unregulated due to
unknown but suspected aggregation areas where shark populations overlap with fishing fleets.
Still, much is not known about the migratory behaviors of pelagic sharks. In Mexico, the
quantity of research about adult great white shark biology has been increasing rapidly, but nearly
nothing is known about juvenile great whites (Hoyos-Padilla et al. 2016). There is a significant
gap in knowledge surrounding pelagic shark behavior due to the difficulties of the subject.
Overall there is a general knowledge of migratory behavior and how sharks interact with their
prey, but still there are gaps when looking at the full lifecycle of certain species.
Sharks are an integral part of the marine ecosystem, so understanding how protected
areas benefit, and fail, them would be exceedingly important in analyzing how we can improve
conservation efforts. The tools associated with conservation change are also important, one being
25
the Integrated Risk Assessment for Climate Change, or IRACC. This assessment was used to
study the vulnerability of sharks and rays in Australia’s waters off the Great Barrier Reef. The
IRACC used the framework for vulnerability caused by climate change in place and applied it to
fishery ecological assessment (Chin et al. 2009). Similar tools and techniques are needed to
improve our assessment of ecological health and subsequently our protection of various species.
To understand the full picture of an animal’s life cycle it is imperative to analyze how
that animal hunts for its prey. Inevitably, due to mere survival tactics, a predator will adjust its
hunting techniques as its prey adjusts its movements. In Australia, a team of researchers focusing
on tiger sharks found that studying the predator-prey relationship between tiger sharks and green
sea turtles unearthed a greater understanding of foraging habits of this apex shark species
(Fitzpatrick et al. 2012). New Zealand boasts a large amount of large prey items for mako sharks,
specifically varied types of tuna. Genetically designed similarly, mako sharks and tuna are well
matched for a perfect predator-prey relationship. In addition to tuna, large gamefish such as
marlin and other sharks are often on the menu for the average mako shark.
Tuna play a huge role in a mako shark’s diet due to the unique structure of the caudal fin.
Both tuna and mako sharks share a distinctly similar caudal fin, which allows the mako shark to
reach the same speeds as the fast-natured tuna (Ebert, Fowler, and Compagno 2013). Figure 5
shows the physical outline of the mako shark and highlights the streamlined features shared by
most sharks, but with the caudal (rear) fin much more equilateral than most other shark species.
For example, the bottom part of the caudal fin in great whites is much shorter than the top. In the
case of the mako shark, they are much more even which gives the mako a much stronger fin to
allow for faster bursts of speed. Figure 6 shows the tuna outline with its similar evenly-
lengthened caudal fin that allows for speed.
26

Figure 5. Mako Shark Anatomy. Source: Bigelow and Schroeder 1948

Figure 6. Tuna Anatomy. Source: Bigelow and Schroeder 1948

The abundance of prey paired with the genetically favorable design of the caudal fin
creates a perfect match for tuna and mako sharks. In addition to the varied species of tuna,
marlins are a popular prey animal for mako sharks due to the speed that mako sharks can reach.
These two main species could affect how the mako sharks hunt and move throughout their
habitats. Understanding prey relationships is important in defining areas of use.
2.3. New Zealand’s Current MPA Policies
The Marine Reserves Act 1971 has been in place since its initiation but potentially could
be replaced by the Marine Protected Areas Act. This new act aims to create a balance between
protecting the marine environment without declining the commercial, recreational, or cultural
27
opportunities that New Zealand provides. In addition, it would establish four types of marine
protected areas and Table 1 shows these four distinct types and a general idea of what would be
protected (MfE 2016).
Table 1. Proposed New Zealand New MPA Designations
Marine reserves
Strictly protected with the purpose of conserving biodiversity in its natural
state. (The same as under the current Marine Reserves Act 1971.)
Species-specific
sanctuaries
Similar to current marine mammal sanctuaries but available to other marine
life such as albatross or great white sharks, with rules focused on the
specific protection of that species.
Seabed reserves
Protect areas of the seafloor and include prohibitions on seabed mining,
bottom trawl fishing and dredging.
Recreational
fishing parks
Recognize that there are areas where the recreational fishing experience
could be improved by providing a preference for non-commercial fishing
for some species. Customary fishing and marine farming will continue.

As a country, New Zealand has been at the forefront of environmental protection
globally. It has over 15,000km of coastline and has the largest exclusive economic zone (EEZ) in
the world. As a culture, New Zealanders value their marine environment not only as an economic
resource, but socially, spiritually, and culturally as well. The government believes that by
investing in the health of their marine environments they are investing in a healthy economy
(MfE 2016). This could create the perfect environment for a potential DMPA to be enacted, both
giving seasonal closures to protect wildlife while still understanding the need for recreational and
economic importance.
28
2.4. Understanding Our Role in Protection
Sharks are a vital part of the food chain, and as we have seen with the growing number of
extinctions, the food chain is incredibly delicate. Disrupting this ecosystem by destroying one of
the top predators will have devastating effects on the rest of the world. Rising sea temperatures
along with other climate change factors have forced larger sharks inland to feed (Ebert, Fowler,
and Compagno 2013). Although many animals we see today have adapted to the changes in the
environment over time, sharks specifically have a limited ability to adapt to the direct effect
humans have had on environmental changes (Rosa 2014). This provides another reason why
protecting areas that are used by sharks, like the mako, is so important.
The film Jaws (Spielberg 1975) rooted the notion that sharks could seek out a particular
person, and that they even have a taste for human flesh. This is categorically incorrect, and the
fact remains that it is still incredibly unlikely to be bitten by a shark, without provocation
(Chapman 2017). Chapman does admit that, yes, bites do happen, but it is much more likely to
be killed by a lightning strike or a vending machine. In general, our understanding about shark
behavior is still so limited it allows false notions and fears to mount when in reality we need to
be focused on increasing protected areas. This is not to say that fatal interactions do not happen,
but we still have a limited knowledge of how or why they happen.
Tiger sharks (Galeocerdo cuvier) around Hawaii’s islands have caused a significant
amount of shark bite incidents over the past few years. A study compared the spatial behavior of
tiger sharks surrounding four of Hawaii’s islands in order to evaluate if there is a connection
based on local movements and the rising number of shark bites in Maui compared to Oahu
(Meyer et al. 2018). Oahu is larger than Maui by a factor of six, based on population size, which
begs the question if there is an environmental difference causing the increase in shark bites. The
29
data used in Meyer et al.’s study revealed that there were more tiger shark detections around
Maui over Oahu, but they noted that the insular shelf surrounding Maui was home to a resident
population who used this particular habitat to hunt.
2.4.1. Existing Work in MPAs
The benefits of spatial management zones, like MPAs, is still under examination;
however, there are studies that aim to close that knowledge gap (Graham et al. 2016). By
studying core habitat use areas (CHUAs) in relation to the surrounding MPAs and EEZs for three
species of sharks in the North Atlantic, the team was able to spatially analyze how the sharks use
these protected zones. They found that expanding protected areas to include territorial waters
would protect all of the habitat use areas that are essential to these three particular species.
In the past few years advances in concepts concerning MPAs have been made and
previous criticisms of the effectiveness they provide has been addressed. The concept of MPA
networks has been introduced to help manage the spatial scale and life-history of pelagic species,
which includes the use of information about overlapping critical habitats (Hooker et al. 2011).
The purpose of these networks is to provide levels of protection that single reserves are unable to
achieve. However, MPA networks require international cooperation and prioritization, which can
be difficult when working with several different cultures.
2.4.2. MPAs vs Dynamic MPAs
To integrate cultural importance with conservation efforts, a plan that has malleable
restrictions could benefit both the ecosystem as well as island cultural traditions. The marine
protected areas that are currently in place only provide significant coverage for benthic and coral
species. The draw of a dynamic marine protected area, or DMPA, is that it would be able to
provide a malleable area that can seasonally shift based on the data. This means, there are areas
30
that can be fished in a particular season and areas that are either no-catch, or limited catch zones
in the same season. This gives freedom to the fishing culture that relies heavily on seafood, while
still providing adequate protection for various species being hunted commercially.
Marine ecosystems are fragile, and any significant shift could be catastrophic including
overfishing of any kind. This project does not aim to minimalize or erase the existing MPAs, but
it does aim to provide a way to better protect pelagic species that move throughout the territorial
waters around New Zealand. Providing a generalized methodology for using tracking data to
identify temporally varying high use areas that can then be used to create seasonal closures
creates an opportunity for local governments to have an integral role in the conservation
conversation.
Areas similar to New Zealand, where conservation practices are combined with cultural
significance, are in need of policies that respect both aspects of island culture. The people that
reside within the region of the Bering Strait, a much harsher climate than the temperate waters of
the Pacific, still struggle with balancing cultural needs of the Aleut, Inupiat, and Yupik people,
with the intensified need for environmental conservation for the Arctic (Siders, Stanley, and
Lewis 2016). This could be a situation where a DMPA would be invaluable, with the ability to
change due to the varying extremes of weather and climate change the Arctic currently endures.
MPAs have been suggested in these areas but, according to Siders et al., they are much more
successful in stable environments, whereas in the Arctic, a more malleable system might perform
better.
2.5. GIS in Marine Conservation
As one could imagine, GIS is vital in the analysis of any spatial dataset. Platforms like
ArcGIS allow users to visually place datasets within their geographic locations. GIS has been
31
used for terrestrial wildlife when analyzing data from animals tracked by GPS collars embedded
with satellite transmitters, like in the case of tracking snow leopards through its remote habitat
(Johansson, Simms, and McCarthy 2016). The applications for GIS in marine conservation have
developed quickly over the past decade and now satellite tags are being used more frequently in
marine science.
Attaching satellite tags to the dorsal fin of a shark is no easy task, but the data output
makes the process entirely worth the trouble. GIS provides the ability to study marine
phenomena in a way that has previously been impossible, including the assessment of disease
spreading in marine populations. With the combination of GIS evolution and remote sensing
advancements, the tools for studying marine animals have grown exponentially. New tools,
methods, and spatial analysis techniques have provided useful resources in the study of wildlife
conservation as a whole (Norman 2008). Using similar terrestrial techniques, and benefiting from
technological advancements, marine researchers have developed new ways to study how marine
animals utilize their environment. These powerful new research tools have become less
expensive and are more readily available, lending to scientific discoveries being made more
frequently (Ebert, Fowler, and Compagno 2013).
2.6. Summary
There are still significant challenges related to marine exploration regarding climate
change, ecosystem development, and resource management that the marine science community
will need to face in the coming decades (Wright 2011). Developing technologies mean that
methods will change as better tactics and tools arise. Wright notes that GIS is a powerful
technology in marine animal conservation, and it is slowly becoming a vital part of
32
understanding how we can improve efforts overall. Spatial analysis of tracking data points
provides a clearer understanding of necessary needs and gaps in current conservation efforts.
By combining technologies and techniques currently used in general wildlife
conservation and using them in marine science, a better understanding of the marine
environments could introduce better tools for conservation. GIS has moved from being a tool
used solely to display data and is now being used to visualize, model, and provide support for
decision making in all aspects of scientific research (Wright 2011). This project aims to add to
this existing collaboration and provide a methodology that can help researchers integrate DMPAs
into the conservation conversation.

33
Chapter 3 Data and Methods
Collecting or having the right kind of data in hand is key to a successful research project. For this
study, several datasets were collected and explored to determine what kind of data is useful in
understanding shark behavior, migration, and current MPA and EEZ locations. Once the datasets
to use were chosen, the next step was to determine how to use them to create a methodology for
identifying viable DMPAs. This chapter discusses the data used and describes the methodology
developed.
3.1. Mako Shark Data
The main collection of data used in this study is from electronic tags deployed on mako
sharks within the New Zealand waters. The original dataset is comprised of the locations in
latitude and longitude of 14 mako sharks. Thirteen of these sharks were tagged on an irregular
schedule between 2012 and 2017, the fourteenth shark was tagged in 2013 off the coast of
Australia as part of another study. This project focused on the 13 within New Zealand’s waters.
This dataset includes individuals of both sexes and various sizes and is predominantly juveniles
(Francis et al. 2018).
3.1.1. Data Source
This shark tagging data was provided by Dr. Malcolm Francis and his team at NIWA, the
National Institute of Water and Atmospheric Research in Wellington, who have been
instrumental in studying shark movements both locally and globally. Dr. Francis and his team
deployed electronic tags on these sharks to study their movements, both temporally and spatially,
and to understand how they use their habitats. They focused on how they moved and classified
each shark’s behavior as either Resident or Travel, considering whether they were within New
34
Zealand’s coastal or oceanic waters. Their results showed that many traversed between the
resident and travel classifications, but that several stayed within the EEZ surrounding New
Zealand. This allowed them to conclude that these sharks were not as nomadic as previously
thought, and that managing the fishing mortality should be on a local scale as well as a regional
one (Francis et al. 2018).
To catch the sharks for tagging, angling was done from a small motorboat while
chumming the waters. Chum is generally comprised of animal, mainly fish, heads, blood, and
intestines which creates a slick across the top of the water. Sharks have a heightened sense of
smell and can locate blood within one part per million (Ebert, Fowler, and Compagno 2013).
Once on the line, the shark was brought alongside the boat and restrained while the boat was in
motion, creating the necessary flow of water over its gills to keep it alive. After collecting
various measuring markers and biological data, tags were attached to the dorsal fin of each shark
by drilling small holes and using stainless steel bolts and washers in compliance with research
standards (Francis et al. 2018).
The 13 sharks were tagged using SPOT5 or Splash tags from Wildlife Computers, a
company based in Washington. All of these tags use satellite communication to relay information
back to the Argos satellites whenever the shark’s dorsal fin breaches the water. Although these
tags did not have GPS functions, the location was determined based on the number of messages
received from Argos satellites. This has varying degrees of accuracy and resulting locations are
classified by an Argos system that is comprised of numbers and letters including: 3, 2, 1, 0, A, B
and Z. According to experimental studies, the location classes 3, 2, 1 and A are considered to be
accurate to approximately 2km. The locations with the quality classes 0 and B are considered
accurate to approximately 5-10km, and class Z locations are considered invalid (Francis et al.
35
2018). Table 2 summarizes the specifics of each quality assessment along with estimated
accuracies. Based on this table, the decision was made to include in this study only those data
points that had a quality rating of 1, 2, or 3. This meant that the range in accuracy was 1000m to
less than 150m. In the greater scape of the ocean, these points were determined to have sufficient
accuracy.
Table 2. Tagging Data Quality Details. Source: Francis 2018
Service
Satellite
Location Class
Estimated Accuracy in
Latitude and Longitude
Standard Location: 3 < 150 m
Calculated from at least four messages
received during the satellite pass
2 150 m <= accuracy < 350 m
1 350 m <= accuracy < 1000 m
0* > 1000 m
Location Service Plus (named
Auxiliary Location Processing in North
America):
• three messages received
• two messages received

A

No estimate of location accuracy
B No estimate of location accuracy
• rejected locations Z (invalid locations)

Prior to delivering the data, Dr. Francis and his team analyzed all the tracks from the
sharks and generated figures using the open-source programming language, R. In addition, the
argosfilter R package was used to filter out Argos improbabilities for location accuracy. They
used a hierarchical Switching State Space Model (SSSM) to filter the Argos locations to estimate
daily locations. This helped the original project with determining the resident and travel
classifications (Francis 2018). Although this DMPA project did not use those designations and
did use individual data points rather than daily summaries, the initial filtering process meant that
the data, once received, was ready for analysis and additional filters could be applied to suit this
particular project on developing DMPA locations.
36
It is impossible to track every shark in the ocean, but this dataset provides a generalized
example of how this particular species uses this specific environment. Dr. Francis and his team
identified the areas in which resident and travel behaviors were focused by creating a kernel
utilization distribution model. Figure 7 shows the results from that assessment. While not
incorporated in to this study, these could be used later to verify the locations of high use areas
identified in this DMPA project.

Figure 7. Kernel Utilization Distribution (KUD) Models. Source: Francis 2018
3.1.2. Tagging Data Exploration
Figure 8 shows the full extent of the data with each shark individually colored. Clearly
there is significant travel among the individuals, with some showing stronger shore fidelity.
According to Dr. Francis, the amount of time spent within the EEZ ranged from 42% to 100%
(the latter in the case of “Nova” or tag number 113678). In addition, five out of the 14 sharks
spent approximately 90% of their time within the EEZ, leading to the overall assessment that
47.3% of the sharks were classified as Resident (Francis 2018). This is contrary to the prior
37
belief that mako sharks were predominantly nomadic. Showing this site-fidelity to inland waters
increases the need for coastal and oceanic protection overall.

Figure 8. Mako Data - Full Extent
Table 3 shows the details of the mako sharks in the dataset, including descriptions of each
individual shark in the project. In addition, this table includes the tag type, which are mostly
SPOT5 tags.
38
Table 3. Tagged Shark Details

Table 4 shows an example of the tagging data with all relevant columns. Note that it
includes specific temporal data along with the quality class assessment, calculated by the number
of satellite signals received and by triangulation, given to each location. This particular table
focuses on a small part of the data from tag number 113680, or Carol.
Table 4. Tagging Data Example
Object ID Tag Lat Long Quality Date Time Datetime
1231 113680 -35.1529 174.2603 3 5/22/12 2:50:00 AM 5/22/12 2:50
1232 113680 -34.73493 174.23661 1 5/22/12 8:22:00 PM 5/22/12 20:22
1233 113680 -34.60964 174.25844 2 5/23/12 4:17:00 AM 5/23/12 4:17
1234 113680 -34.61453 174.25644 3 5/23/12 4:41:00 AM 5/23/12 4:41
1235 113680 -34.26348 174.44709 1 5/23/12 7:53:00 PM 5/23/12 19:53

The first step in understanding the shark tagging data was to identify each individual
shark as it moved through space and time. One of the ways to achieve this was by using
ArcMap’s tool “Tracking Analyst”. By using the temporal data provided for each individual data
point, a timeline was created that could show from the first tagging date to the last known
position, how all sharks moved through the area. Once individualized by tag number, the
39
movement was animated, which gave a clear picture from one day to the next how each shark
moved throughout space and time.
Visually understanding how the sharks moved helped in extracting seasonal information
for each shark. Searching for seasonal changes in behavior by using the visualization of these
track lines helped in understanding if there was a strong correlation between movement and time
of year. Although the dataset was complete with hour, minute, and second times, only the month,
day, and year was used. This choice was made based on the need for a DMPA to change
seasonally, not day-to-day.
The seasons in the southern hemisphere are opposite to those in the northern hemisphere.
Based on previous works, with some minor variations, the seasons for this project were defined
as shown in Table 5. One variation can be found in the behavioral project from Dr. Francis that
considers March as the late summer, but June as early winter (Francis 2018). For the purposes of
this project, it was decided to keep the seasons equally balanced. As explained below in the
methodology, this seasonal classification was used when manually separating the track lines into
seasonal chunks.
Table 5. New Zealand Season Breakdown
Season Months Included Month Number
Spring September, October, November 9,10,11
Summer December, January, February 12,1,2
Fall March, April May 3,4,5
Winter June, July, August 6,7,8

To see the seasonal changes in space and time two methods were tried, but inevitably
they were not included in the final analysis. There is a useful tool within ArcGIS Pro called the
Data Clock that produces a data visualization showing the distribution of the data points over
time (e.g. by month and year). Using this tool was helpful only in seeing that there was a
40
significant temporal gap in the data. Although noting the gap in the data was useful, it was
ultimately decided that the Data Clock was not the best visualization for the project as a whole.
The data points were not evenly collected over time, the dates of collection were irregular and
frequency varied depending on individual shark behavior and collection opportunities, not on
any kind of structured sampling frequency. This meant that the Data Clock showed clusters
whenever several data points were collected close in time but did not show the actual number of
sharks at the time.
For the same reason, hot spot analysis could not be used since the data points did not
indicate anything other than more frequent collection of data in one location. Again, the aim of
this spatial analysis was to locate areas of high use from several sharks, not just one. It was
determined that hot spot and kernel density analysis would not be useful in this particular project.
Therefore, unfortunately, the Space-Time Cube analysis toolkit within ArcGIS Pro could not be
used.
3.1.3. Seasonal Water Temperature Changes
Another data exploration surrounded the issue of climate shifts that could potentially
affect shark migration and behavior. In the ocean, water temperatures can have a direct effect on
how a fish moves through certain areas. Some sharks have the capability to adjust their body
temperature; as discussed previously in this paper, the mako shark does have this trait and can
make its body temperature higher than the surrounding waters. This allows the shark to move
into colder or warmer waters easier than a shark without this ability.
The slightest change in temperature can create issues for smaller aquatic organisms like
phytoplankton. This change can cause a ripple effect in the food chain, forcing smaller species of
krill, fish, and microorganisms to move to different waters than usual. This inevitably creates a
41
shift in prey behavior which can affect predator behaviors as well. Because of this connection,
water temperature data was searched for that could track the temperature changes throughout the
year. Seasonal changes can be either normal or abnormal based on the areas of study and
temperature fluctuations help to indicate why a species makes adjustments in their normal
migrations. There was one dataset that seemed viable to include in analysis, but it was eventually
used in verification of the results rather than initial analyses. Further research in temperature
changes could provide more insight into whether this plays a significant role in behavioral
changes within sharks, but for the purpose of this thesis project there was no need.
3.2. Data Preparation
Once data are compiled, preparing them for analysis is the next step. This includes an
overview of how the data can be integrated into the project and how it will best be displayed.
Visualization is an important step of data exploration and choosing the correct projection is vital
in correctly analyzing the data when it is displayed. The projection normally used in New
Zealand is the Universal Transverse Mercator (UTM) 2000 and NZGD2000 is the official datum
used for positions within New Zealand, so this is the datum that was used for this project. The
original data was converted from the SSSM locations in latitude and longitude to UTM locations
centered around zone 60, which is 174ºE -180º (Francis 2018).
The first step of the data preparation involved adding a field to the tagging data into
which the season associated with each date could be inserted. Next, individual shark track lines
were created using the “Point to Line” tool. After verifying the resulting lines contained the
attribute data needed to identify them, the next step was to separate the lines based on seasonal
breaks. To begin this process, a new layer was created for each shark to allow individual
seasonal breaks to be more easily identified. Then, each shark’s track was displayed with
42
sequential data points labeled according to season. By manually locating the two adjacent points
on each side of a seasonal change, the track line was edited using the “Split” tool. This created
separate season line segments that contained the same attributes as the original. In the end, this
resulted in one to four sets of separate seasonal track segments for each shark, dependent on how
long the shark was tagged.
From there, using the Buffer tool, a polygon was created around each seasonal line
segment with a radius of 32m, which was the determined average distance a shark would travel
in a day’s time. Once all of the individual shark seasonal polygons were recombined into a single
“Seasonal Polygons” dataset, the tagging data were now transformed into seasonal polygons that
could be used to determine overlapping areas of importance.
3.3. Methodology to Identify Core Areas for DMPAs
The main goal of this project was to create a generalized workflow that can be applied in
other locations for other species. Having a clear step-by-step explanation means that others will
be able to use the same methodology and create results to help within their own communities.
This section discusses the use of ModelBuilder and shows flowcharts with defined steps on how
to create designated areas for protection. This project went through several variations of how to
achieve designated areas of high use. The aim of the project, however, stayed the same—the
need to identify areas that are used more frequently and could benefit from seasonal protection.
3.3.1. The “Spaghetti and Meatballs” Method
In order to determine the areas where the most use occurred, a Count Overlapping
Polygons method was needed, named by its creator the “spaghetti and meatballs” method
(Honeycutt 2012). Essentially this method uses the concept of cartographic “spaghetti” and
creates centroid points, called “meatballs,” to count the overlapping polygons. The process
43
begins by intersecting (Union) all overlapping shark polygons (buffered track segments) for a
single season, resulting in a collection of many tiny non-overlapping polygons, the “spaghetti”
polygons. Creating the meatballs was achieved by using the “Feature to Point” tool on these
spaghetti polygons and ensuring all centroids fall “inside” the polygons. From here, the next step
is to perform a spatial join to count the number of original overlapping polygons at each
meatball.
Using the ModelBuilder tool in ArcGIS Pro was vital in creating a user-friendly
workflow that could be replicated by others. Figure 9 shows the final model workflow used in
this project, utilizing the “spaghetti and meatballs” method. Several steps are dependent on input
features being correct, which means that initially the dataset has to include specific ways to
identify each track line and season, hence the importance of carrying over attributes.
44

Figure 9. ModelBuilder Workflow
3.3.2. Identification of Core Areas for DMPAs
The next step was to display the high use areas visually. Each season has some polygons
with high numbers, indicating locations more sharks frequent. This means these areas are used
more than others which could indicate greater importance. Once these areas have been identified,
an assessment can be made on the value of these areas as locations for seasonal closures. Using
different colors and textures to identify the areas separately was an important factor. Being able
to clearly see these locations means that the results are far more user-friendly and easier to
understand.
By displaying the symbolized join count overlap, areas where there were concentrations
of sharks are readily evident. These are the areas of high use. To make these clearly visible, the
45
polygons with a join_count number higher than a specific number were selected and extracted.
For the purposes of this demonstration, a join_count of 3 or higher was used, except in one case
described below. This meant that these areas had seasonal polygon overlaps of at least three
sharks, not one shark using that area three or more times a year. This number was chosen,
somewhat arbitrarily, as it is the lowest possible number that still included enough data for
analysis. For a dataset composed of tracks from only 13 sharks, one shark in an area could be a
fluke, but 3 can indicate a pattern. The summer season, December to February, had the least
number of data points and the join_count cut-off was dropped to include 2 or more for data
visualization purposes. Future users of this method will need to choose their own cutoff point
based on their data and knowledge of the species being studied.
Using the “Dissolve” tool on the join_count numbers, seamless weighted polygons were
created. The areas with the critical overlap count were selected and became their own layer by
using the “Copy Feature” tool.
This is the process that can be used and edited by others for their own projects. Using a
program called Lucidchart, a workflow was created to show the generalized steps of the project,
beginning with gathering data, and including steps to filter and digitize data accordingly. Figure
10 shows this workflow. The main goal of this workflow is to visually show the steps needed to
get the end result, although this process is unique to this analysis of the mako shark tagging data.
46

Figure 10. Project Workflow

Table 6 shows each of the steps outlined with details required for this particular project
and DMPA.
Gather Data
Mako Shark
Data
Prey Data Other Data
Filter Digitize Polygon
Display Data
Create Track
Lines
Formalize Process
Display Individual
Shark Tracks
Manually Split
Lines at Seasonal
Changes
Create Buffer of
32km/17 nm
Create Seasonal
Dynamic MPA
Quality = '1,2,3'
Digitize Buffers to
Polygons in
Feature Class
Identify Seasonal
Overlap
"Join_Count"
Count Overlap
"Spaghetti &
Meatballs"
Digitize Polygons
of High Use Areas
Point To Line
47
Table 6. Detailed Workflow Outline
Step General Method Specific Requirements for mako DMPA
1 Gather data set
Minimum of 1000 data points over 3 years for tracking
areas of heavy use
2 Extract useable data with best quality Extract data with “quality” score of 1,2, or 3
3 Input data into GIS program Add data to ArcGIS Pro
4
Create individualized tracks to best
see data and area uses
Use expressions to “select layer by attribute” from the
Mako Sharks dataset, use “tag” column data to identify
individual sharks.
Then use “XY Table to Point” to create separate tracks
5 Make use of temporal data
Convert in Excel to one column for date and time, use
“Text to Columns” feature
6
Use the Tracking Analyst tool in
ArcMap to study movements
throughout time
Separate track lines for each individual tag number to
ID isolated sharks
7 Create track lines in ArcGIS Pro
Use “Point to Line” tool to sequentially connect data
points
8
Create Buffers around seasonally
segmented lines
Use editing session to digitize, create a buffer of 32
km to account for day-to-day movement of sharks
9
Digitize Polygons for Visual
Representation
Use “Spaghetti and Meatball” method to create
polygons
10
Identify locations with significant
overlap
Dissolve polygons based on join_count number
11
Display Polygons of closures for
DMPAs
Use created polygons to show areas where seasonal
trends occur and mark those as closures for the
dynamic MPAs
3.4. Summary
Using these designated areas, a selection can be made where the count is above the
number of overlaid seasonal polygons needed. In this case, greater than 3 is needed for the
seasons fall, winter, and spring, and 2 needed for the summer season. With this selection, a new
layer is created where polygons with only those counts exist, and these areas are the high use
areas for each season. Showing all the polygons together suggests the areas that can be used to
make up a DMPA in the area. Seasonal closures can include all, or parts, of the highlighted areas
to ensure species numbers increase instead of decline.
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Chapter 4 Results
The workflow in this project was able to identify areas that might be considered as DMPAs for
mako sharks in New Zealand. It is important to repeat, this project was not intended to provide
details that would enable authorities to implement these policies, but merely is showing how
appropriate data and GIS techniques can be used to delineate areas where DMPAs could provide
increased protection for pelagic species. This chapter shows the results of each step of the
workflow.
4.1. Workflow Results
The beginning steps included gathering viable data and filtering out any erroneous or
unnecessary information. Looking at the original dataset, there were over 12,000 data points
available. Filtering out by quality, based on the Argos legend provided by Dr. Francis, the final
dataset ended up at just over 5,000 data points. From here, to achieve accurate results, only the
data points with a quality rating of 1, 2, or 3 were used. Next, changing the symbology and
displaying the data points by color-coding each shark was done to best see individual trends.
4.1.1. Creating Track Lines & Seasonal Splits
Once the data was gathered and displayed, creating track lines was the top priority.
Figure 11 shows a closer look at the initial dataset, colorized and separated, to indicate each
shark. Without track lines it is nearly impossible to follow each line, which is why using the
“Point to Line” tool was so helpful. Figure 12 shows the datapoints with the track lines included;
however, it is still difficult to see each track individually. Understanding how to properly display
the data is essential in creating a valuable result. Initially, the idea of separating each polygon
49
into seasonal colors was attempted, but the end result was still impractical. It was determined at
this stage that using the buffers as polygons to determine overlap was the best course of action.

Figure 11. Individual Mako Sharks

50

Figure 12. Data with Track Lines
The next step was to create seasonal breaks in each line, determining what areas were
traveled in within each season. By using the “Edit” and “Modify” options in ArcGIS Pro, the
“Split” tool was used to manually go through each track line and “break” the lines where the
season changed. Then, by selecting each separate line segment, a new layer was created for each
individual shark for each season.
4.1.2. Buffered Polygons and Spaghetti
The next step after splitting the lines seasonally was creating a buffer around each line
segment. This indicated the approximated area a mako shark could travel based on randomly
selected individual sharks and several data points. The determined buffer radius was 32km,
51
which would provide enough of an area that could be seen and protected. Once each buffer was
created, the final few steps could be identified. Figure 13 shows the buffered seasonal polygons
used in analysis. This initial jumble shows all the areas that overlap, but this is illegible and
needs to be altered further.

Figure 13. Buffered Seasonal Tracks
Creating the “spaghetti” layer included combining all the separate seasons, with
corresponding attributes, into one layer with overlapping buffered zones. All the polygons in this
layer were then intersected. Once this was done, each polygon overlap area could be identified
and the next step of creating the “meatballs” could be implemented.
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4.1.3. Meatball Spatial Join
Figure 14 shows the centroid points (meatballs) alone for the winter season, while Figure
15 shows those points within the respective polygons made by the “spaghetti”, creating the
“SpagMeat” or spaghetti and meatball layer for winter.

Figure 14. "Meatball" Centroid Points
53

Figure 15. Spaghetti and Meatballs
After these centroid points are created, the next step is to overlay the original season’s
polygons to count how many polygons are overlapping at each point. Using the “Spatial Join”
tool creates a join_count column in the meatballs attribute table which can be used later to sort
out the areas with the largest count. Ensuring to choose the “one-to-one” option in the parameter
box and “keep all target features”, there is an option to choose how to filter via the “Field Map”
parameter window. In this project, the SHARK_ID column was chosen as the designated
identifier.
A second “Spatial Join” appends the meatball counts to each spaghetti polygon. Figure 16
shows this result for the winter season with only polygons with more than 0 count are included.
54
From here, choosing to display only those areas that had the required join_count number was
done by simply using the “Select by Attributes” tool and creating a new query.

Figure 16. Union of Spaghetti and Meatballs
4.1.4. Selecting and Symbolizing Join Count
Figure 17 shows the areas where there are 2 or more sharks that overlapped, indicating
the overlapped polygonal areas that are used more frequently. Figure 18 shows the same map
with the areas designated as high use areas based on the fact 3 or more sharks used the area. This
would inevitably become part of the final results map for the winter season. The darker red areas,
just north of Auckland, show that more sharks overlapped in these particular areas than others.
55

Figure 17. Winter Join Count Areas
56

Figure 18. High Use Areas for Winter
4.2. Final Result
Once these counted areas have been isolated, a map of the high use areas was created. By
selecting the overlap where 3 or more sharks converged (2 or more in summer), the highlighted
regions were designated as high use areas. They could then be extracted as polygons in their own
layer and symbolized accordingly. Figure 19 shows the final results of the project, highlighting
each seasonal area that can be used for closures or regulations.
57

Figure 19. Seasonal High Use Areas
Each season has its own color or pattern that distinguishes it from the rest. There is some
overlap as well as solidarity among use areas. For example, the areas associated with fall are
predominantly close to shore and on the western side of the north island of New Zealand,
whereas the summer season is predominantly on the eastern side and closer to the southern
island. There is overlap of several of these polygons which indicates areas of heavy use in
several seasons. North of Auckland, nearing the northern tip of the island, is an area where 3
seasons overlap.
58
4.3. Analysis of Results
The results of this project could indicate areas of extreme importance to mako sharks
year-round, which could potentially be places these sharks breed or mate. Understanding these
locations by studying behaviors or populations of the sharks, could lend more knowledge of the
full life-cycle of this species of shark. These results show areas throughout the year that are most
used by shortfin mako sharks off the coast of New Zealand. This is not representative of every
single shark in the water, but it does show the areas that could have increased conservation
efforts so that this particular species of shark is protected throughout the life-cycle.
While this project was only intended to develop a workflow for finding high use areas, it
is possible to verify the results by comparing them with other spatial datasets that may
corroborate the results. One way to verify the results make sense is to apply a prey dataset in
conjunction with seasonal shark areas. Figure 20 shows the overlap between mako sharks and
Albacore tuna in the fall season. The darker red, or maroon, spots are annual distribution location
hot spots for Albacore tuna in these waters. Although this is a generalization, you can see they
favor the western side of the north island and parts of the southeastern sides as well. The mako
sharks overlap is minimal and may indicate that they have another desired prey in these areas
during the fall months.
59

Figure 20. Fall Tuna Overlap
However, when we look at a different season, we can see that there is arguably a strong
correlation between the two datasets. Figure 21 shows the same prey fish, Albacore tuna, in the
spring season. Here a clear overlap of the two can be observed. The locations that are highlighted
in a bright purple indicate the areas where mako sharks are most frequent in the Spring months.
Paired with the maroon locations, the hot spots of the tuna, this map shows that there is
significant overlap, potentially indicating that there is increased predation on Albacore tuna at
this time. This indicates that there could be a strong relationship between the distribution of
mako sharks and their prey, which would make sense in the overall assessment of the lifecycle of
a shark. They would move and hunt in the same areas as their prey would, meaning that if you
protect one you can easily protect the other species.
60

Figure 21. Spring Tuna Overlap
Again, this is only one species’ annual distribution that could be used to verify the
locations made sense. There are several prey species that could be used to examine other
overlaps.

61
Chapter 5 End Results: What Does It Mean?
This project has shown that the use of GIS in identifying locations for DMPAs is not only
possible but may be incredibly valuable. Using techniques in spatial mapping allows data to be
visually accessible for scientists and local communities alike. This creates a bridge between the
conservationists and the locals alike. Creating clear indications of zones that can be used as
protected areas means that the species can be taken care of, as well as cultural traditions
respected. Areas were highlighted that are used most by this pelagic species of shark, based on
the data provided. Using this information, scientists and government officials could implement
conservation regulations based on these findings.
5.1.1. Changes and Improvements
The first and hardest struggle with this project was finding accessible and useable data.
Going back again, pairing up with an organization with access to data would have been
beneficial. Focusing on an area within the US territory would also have been a good idea, but
having already had background on New Zealand specifically, as well as local shark species, it
was decided to continue with the original focus. Another issue that arose was finding other
relevant data within the same time span and with valuable attributes. Locating accurate sea
surface temperature data would have been interesting in looking at the changes in use from
season to season.
The limitations within the project mainly stemmed from the lack of access to pertinent
data, but also involved time issues. The late-term change in data meant that there was a lot of
make-up to do quickly as well as time crunches to get the data to a workable set. More time
would mean more ability to delve deeper into the dataset and discover what other questions
could be answered. Some of these could center around the differences between mature adult
62
sharks and juveniles, do they use the same areas or do they naturally separate? Do females and
males patrol the same areas? These questions could be explored with more time to look into both
this dataset and other existing datasets.
5.1.2. Future Work and Applications
This project only used one particular type of prey to verify the end results. Although
Albacore tuna is a popular prey item for mako sharks, they feed on several different species of
fish, marine mammals, and gastropods. Having been shown to be residential as well as nomadic,
these sharks have a plethora of food to choose from. Using this dataset with another type of prey
fish, like the marlin or swordfish, could produce different results based on the pelagic nature of
both animals. Something different might be seen by using gastropod data, like that on Pacific
octopus numbers, which could potentially show a far more residential behavior.
The applications for a design like this are immense, but one application seems potentially
viable. Creating seasonal closure areas from spatial datasets and inputting them into a mobile
platform that anyone could access means that local fisherman would know which areas have
catch limits or restrictions and which areas are free to fish within. Giving only a general closure
location might dissuade poachers from landing more than necessary, or the app could require a
vessel to register itself and input catch numbers to be able to use it. These numbers can then be
verified by government fishing agencies as necessary, or volunteers willing to make the checks
themselves.
In addition to a mobile app for fisherman, locals would also be able to modify an app to
include locations for recreational tourism. Knowing the areas that are frequented by certain
animals could mean tourism increases in those areas, which would boost the economy. Finding
the line between a booming tourism industry and overusing resources would be a necessary
63
assessment, but it could be beneficial in many aspects. The main purpose of applications like this
would be to bridge the gap between the devout scientists and the day-to-day conservationists.
Hopefully everyone would want a healthier ocean so that the species within remain healthy and
abundant. Creating opportunities for environmentally conscious individuals to become a part of
that design would mean that more people would be interested in becoming involved.
The ability to find spatial patterns of a migratory apex predator in its environment is
useful on all scales of environmental conservation, both marine and terrestrial. This is why
environmental research is so important in today’s world when we can clearly see significant
shifts in food sources and climate changes. The works reviewed and discussed in this paper are
all concerned with understanding how each organism has an effect on the environment and how
they utilize their surroundings. By studying the movements of an apex predator like the mako
shark within MPAs, or outside the static protection zones, we can begin to fill the gaps of
knowledge about the benefits of spatial management zones and how to improve and implement
them globally.
When we protect ecosystems and habitats for apex predators, the whole food chain
benefits. We have seen this with wildlife control; by enabling strict hunting seasons, the
population of, say white-tailed deer, has maintained a healthy number without risking extinction
(McShea 2012). Wildlife reserves and state parks have provided havens for species that might
otherwise be extinct. Marine protected areas have also helped to conserve marine species from
being overfished or hunted.
Marine conservation, and environmental conservation as a whole, is constantly evolving
and so should this project. Different methods yield different results, for this project the results
that were needed were achieved, but every species has unique requirements for sustainability.
64
The potential for DMPAs is impressive but difficult and each year will bring new technology
that can be used to better understand the marine world. Images of data overlaps, data
explorations, and results will show that this project focused on the usefulness of GIS in spatially
mapping specific areas for conservation. This could hold people accountable for how they
interact with the ocean.
Accountability goes both ways, especially when you consider local island culture and its
connection to the ocean. Governments being transparent enough to provide raw data, easily
comprehended maps, and policy regulations can mean the increased and sustained involvement
of entire communities. This is another reason that GIS is vital to projects that have high numbers
of datasets. The ability to use large amounts of data and visually represent them in ways that are
easily understood creates the accessibility that projects, like marine conservation, desperately
need.
Building legislation around protected areas is difficult, as is any legislation. It takes time
and resources that some countries do not have. This project aimed to show that GIS can be used
successfully in marine conservation efforts, and it has achieved that. Based on the model and
workflow given, similar research can be done on different species worldwide. This project does
not mean that the New Zealand government will implement this DMPA, but the hope is that it
could open a discussion about the gaps in pelagic species conservation and what we can do to
bridge those gaps. Our focus, now more than ever, needs to be on the health of the ocean. Each
step we take in species conservation is a step in the right direction. Using tools, like GIS, to
create integrative maps and research to provide conservation ideas means that we continually
work towards a healthier ocean.

65
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Abstract (if available)

Abstract Analyzing pelagic shark behavior is an ongoing challenge due to the highly migratory nature of these animals, as well as outside threats such as overfishing and climate change. Increased protection of vital habitats is essential in combating declining species numbers. Although some shark species, like the shortfin mako (Isurus oxyrinchus), have made a steady comeback in the last decade, there is still significant room for improvement. Comprehending the connection between how sharks use their environment and move between protected territories can benefit our understanding of shark behavior and conservation as a whole. By analyzing shark movements over time and creating visual representations of core habitat use areas, an assessment can be made on the potential for implementation of seasonal dynamic marine protected areas (DMPAs) in New Zealand’s waters to aid in pelagic conservation. ❧ Starting with a large spatio-temporal dataset of tagging data collected for 13 mako sharks over five years, these data points were first cleaned and filtered in order to create individual shark track lines for visualization of the data as a whole. Next each shark’s track was divided into seasonal chunks and these were buffered to a 32km wide zone, which, based on the data, accounts for an average day’s movement of a mako shark. This collection of seasonally tagged polygons represent the areas used by each shark in each season. The next step was to intersect and count overlapping seasonal polygons to identify the “high use” areas. The result is a map showing areas where seasonal closures might benefit overall conservation, the areas to consider as the core for future DMPAs.

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Asset Metadata

Creator Taylor, Marie Anne (author)

Core Title Using GIS to identify potential dynamic marine protected areas: a case study using shortfin mako shark tagging data in New Zealand

School College of Letters, Arts and Sciences

Degree Master of Science

Degree Program Geographic Information Science and Technology

Publication Date 10/03/2019

Defense Date 08/29/2019

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag DMPA,dynamic marine reserves,mako sharks,marine protected areas,marine science,MPA,New Zealand,OAI-PMH Harvest,satellite telemetry,shark tagging,spatial data

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Kemp, Karen (committee chair), Lee, Su Jin (committee member), Loyola, Laura (committee member)

Creator Email marietay@usc.edu,mataylor1073@gmail.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c89-222935

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Legacy Identifier etd-TaylorMari-7836.pdf

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Rights Taylor, Marie Anne

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