ABUNDANCE AND DISTRIBUTION OF HUMPBACK WHALES (MEGAPTERA NOVAEANGLIAE) ALONG THE OUTER COAST OF WASHINGTON AND OREGON

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Title

Eng ABUNDANCE AND DISTRIBUTION OF HUMPBACK WHALES (MEGAPTERA NOVAEANGLIAE) ALONG THE OUTER COAST OF WASHINGTON AND OREGON

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Eng 2021

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Eng Foster, Hillary

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Eng Thesis_MES_2021_FosterH

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ABUNDANCE AND DISTRIBUTION OF HUMPBACK WHALES (MEGAPTERA
NOVAEANGLIAE) ALONG THE OUTER COAST OF WASHINGTON AND
OREGON

By
Hillary Marie Foster

A Thesis
Submitted in partial fulfillment
of the requirements for the degree
Master of Environmental Studies
The Evergreen State College

© 2021 by Hillary Marie Foster. All rights reserved.

This Thesis for the Master of Environmental Studies Degree
by Hillary Marie Foster
has been approved for
The Evergreen State College
By

____________________________________
John Withey, Ph.D.
Member of the Faculty

March 12, 2021

ABSTRACT
Abundance and distribution of humpback whales (Megaptera novaeangliae) along the
outer coast of Washington and Oregon
Hillary M Foster
Humpback whales (Megaptera novaeangliae) are highly migratory marine mammals
whose migratory corridors overlap with human activities, making them susceptible to
potentially fatal human interactions. To mitigate the negative impacts anthropogenic
threats are having on humpbacks’ long-term survival, it is vital to understand what
specific threats they face. Along the US West Coast, their biggest threats are
entanglement in derelict fishing gear (i.e., crab pots) and fatal collisions with vessels.
Effective conservation management strategies rely on the continual update of species
abundance and density estimates in a given geographic area. These estimates provide our
best insight into the severity of specific threats and can be used by a multitude of
agencies to develop more effective conservation management practices. Marine mammal
abundance surveys are vital for determining high density areas and detecting changes in
populations. Seasonal estimates provide more detailed look at when and how long these
animals are spending in a particular area during a given time of the year. Distance
sampling is the mostly widely used technique for estimating abundances of wild animal
populations. It allows for estimation of abundance in an area without needing to count
every animal within the area of interest. Cascadia Research Collective and Washington
Department of Fish and Wildlife jointly designed and implemented Distance sampling
surveys off the coast of Washington State and Oregon from 2011 – 2013 to obtain
seasonal abundance and density estimates humpback whales. Humpback whale sightings
data collected during these surveys were analyzed using the software Distance following
‘conventional distance sampling’ and ‘multiple covariate distance sampling’
methodologies. Hazard-rate with visibility as covariate was determined the model of best
fit of the detection function over distribution of perpendicular sightings for large whales
based on lowest ΔAIC. In total, 2,044 nmi were surveyed for whales between 2011 –
2012. Data from 2013 was omitted due to inconsistent survey coverage. Modeled results
estimate the total abundance to be 2,205 (CV=0.26) humpbacks and an estimated density
of 6 (CV=0.26) humpbacks per 100 nmi2. Seasonal effort was variable, with a range of
224-1,061 nmi surveyed. It was estimated that there were 276 (CV=0.6) humpbacks in
the spring, 1406 (CV=0.32) in the summer, and 524 (CV=0.6) in the fall. Although
estimates appear to differ by season, the 95% CI for abundance and density estimates
overlap, therefore there is no significant difference in seasonal estimates. Although there
have been numerous studies estimating humpback abundance and density estimates along
the US West Coast, this is the first to assess seasonal trends. These results provide
general insight in the probable seasonal distribution trends and can aid in the
understanding of humpback whale seasonal variations within this study area.

Table of Contents
Table of Contents ..................................................................................... iv
List of Figures .............................................................................................v
List of Tables ........................................................................................... vii
Acknowledgments .................................................................................. viii
: Introduction ................................................................................. 1
: Literature Review ........................................................................ 4
2.1 Humpback Whales (Megaptera novaeangliae) ..................................4
2.1.1 Evolutionary History ...................................................................................................... 4
2.1.2 Species Description ........................................................................................................ 9

2.2 Policies, Conservation Status, and Management ............................12
2.2.1 Policy and Conservation Status .................................................................................... 13
2.2.2 Management ................................................................................................................. 19

2.3 Monitoring Methods ..........................................................................21
2.3.1 Distance Sampling........................................................................................................ 22

2.4 Line-Transect Surveys and Abundance and Density .....................27
2.5 Current Threats .................................................................................30
2.5.1 Ship Strikes .................................................................................................................. 30
2.5.2 Entanglement................................................................................................................ 38
2.5.3 Other Threats ................................................................................................................ 40

: Methods ..................................................................................... 43
3.1 Survey Area ........................................................................................44
3.2 Data Collection ...................................................................................46
3.3 Data Analysis ......................................................................................47
3.3.1 Estimating Probability Density Function and Cluster Size .......................................... 48

Results .......................................................................................... 51
4.1 Survey Effort and Model of Best Fit ................................................51
4.1.1 Survey Effort ................................................................................................................ 51
4.1.2 Model of Best Fit ......................................................................................................... 55

4.2 Humpback Abundance and Density Estimates ...............................57
4.2.1 Raw Data ...................................................................................................................... 57
4.2.2 Abundance and Density Estimates ............................................................................... 60

: Discussion.................................................................................. 63
5.1 Humpback Whale Abundance & Density .......................................63
5.2 Conclusion ...........................................................................................66
Literature Cited ........................................................................................67

iv

List of Figures
Figure 2.1: Evogram detailing evolutionary history and important morphological
adaptions of whales dating back 55 MYA. (Image adapted from Zimmerman, C. (2009).
The Tangled Bank: An Introduction To Evolution. Roberts and Company Publishers.) ... 5
Figure 2.2: Northern Hemisphere (top) and Northern Hemisphere (top) and Southern
Hemisphere Humpback whale. (Image adapted from Clapham, P. J. (2018). Humpback
whale: Megaptera novaeangliae. In Encyclopedia of marine mammals (pp. 489-492),
Academic Press.)............................................................................................................... 11
Figure 2.3: Identification, location, and current conservation status of the 14 identified
distinct population segments of humpback whales (numbered circles) and their associated
feeding areas (green circles). (Image adapted from NOAA Fisheries
https://www.fisheries.noaa.gov/species/humpback-whale). ............................................. 13
Figure 2.4: Measurements that are recorded during line transect surveys. An area size A
is sampled by following along a line of length L. An objected is detected at distance r
from observer and sighting angle θ is measured to calculate perpendicular distance x. The
distance the object is from observer parallel to transect at detection is z = r * cos (θ).
(Image adapted from: Buckland, S.T., Anderson, D.R., Burnham, K.P. and Laake, J.L.
(1993) Distance Sampling: Estimating Abundance of Biological Populations. Chapman
and Hall, London. 446 pp.). .............................................................................................. 25
Figure 2.5: Updated model-based densities for false killer whale, short-finned pilot
whale, sperm whale, and Bryde’s whale from line-transect surveys (grey lines) for years
2002 and 2010 from 15 systematic line-transect ship surveys conducted by National
Oceanic and Atmospheric Administration (NOAA) Southwest Fisheries Science Center
and the Pacific Islands Fisheries Science Center between 1997 and 2012. Black dots
represent locations of sightings. (Figure adapted from Forney, K. A., Becker, E. A.,
Foley, D. G., Barlow, J., & Oleson, E. M. (2015). Habitat-based models of cetacean
density and distribution in the central North Pacific. Endangered Species Research,
27(1), 1-20.) ...................................................................................................................... 29
Figure 2.6: Map showing highest risk areas of a lethal collision for humpback (top) and
fin whales (bottom) along coast of Vancouver Island, BC and at the mouth of the Strait of
Juan de Fuca from GAM model estimates. Whale sighting data obtained from aerial
surveys from 2012 – 2015 and shipping data was obtained from 2013 AIS ship traffic
data. (Image adapted from: Nichol, L.M., Wright, B.M., Hara, P.O., & Ford, J.K. (2017).
Risk of lethal vessel strikes to humpback and fin whales off the west coast of Vancouver
Island, Canada. Endangered Species Research, 32, 373-390.) ......................................... 37
Figure 3.1: Designated survey area outlined in black for systematic line-transect surveys
by Cascadia Research Collective and WDFW on vessel G.H. Corliss from 2011 – 2012.
Regions are identified as [A]: Washington; [B]: Northern/Central Oregon. Dashed box
outlines the Olympic Coast National Marine Sanctuary. Red lines are the navigated
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transect lines and their corresponding transect number. Major submarine canyons are
identified. Major cities labeled. Light grey lines represent bathymetric contours............ 45
Figure 4.1: Fitted hazard rate with visibility as covariate MCDS model (red curve) to
distribution of large whale perpendicular distances (histogram). ..................................... 56
Figure 4.2: Distribution of raw sightings of humpback whales made during line-transect
surveys from 2011 – 2012 and important underwater canyons identified. Geographic
strata include: (A) Washington and (B) Northern/Central Washington. Dashed outline
represents boundary for Olympic Coast National Marine Sanctuary. .............................. 59

vi

List of Tables
Table 4.1: Summary of on-effort surveys conducted from G.H. Corliss line-transect
surveys by Cascadia Research Collective and WDFW from 2011 – 2012 along coast of
Washington (WA) and Northern/Central Oregon (OR).................................................... 52
Table 4.2: Summary of total effort (nmi), number of transects covered, and average of
environmental variables measured for each survey from G.H. Corliss line-transect
surveys by Cascadia Research Collective and WDFW from 2011 – 2012 along coast of
Washington and Northern/Central Oregon. ...................................................................... 52
Table 4.3 Number of humpback whale sightings, effort, encounter rate, mean cluster
size, and average perpendicular distance by sea state from G.H. Corliss line-transect
surveys by Cascadia Research Collective and WDFW from 2011 – 2012 along coast of
Washington and Northern/Central Oregon. ...................................................................... 54
Table 4.4: Summary of model selection statistics and parameter estimates for models
proposed to fit perpendicular distance data for large whale sightings from G.H. Corliss
line-transect surveys by Cascadia Research Collective and WDFW from 2011–2012
along the coast of Washington and Northern/Central Oregon. Model of best fit ( hr + vis )
chosen by lowest AIC value (ΔAIC = 0). ......................................................................... 55

vii

Acknowledgments
This thesis would not have been possible without the support from the incredible
biologists at Cascadia Research Collective. To Annie Douglas, my research advisor, your
unwavering support and guidance throughout each stage of my thesis process was
integral to my success. Thank you for your profound belief in my work and valuable
feedback. To Kiirsten Flynn, my internship supervisor, thank you for your mentorship
and overall support. I especially want to thank you for inviting me out that day to help tag
humpback whales. It was a truly incredible and formative experience. Thank you both for
being excellent mentors and further inspiring my passion for whale research. You are
both excellent role models and demonstrate that women can be successful research
biologists. My sincerest thanks to John Calambokidis for providing me the data along
with your knowledge and expertise. I enjoyed hearing about how you used to do all these
estimates on paper. That made me appreciate the software Distance, even when it was
giving me such a headache. Thank you to Alex Zerbini for your technical support with
distance analyses. You were influential in shaping my data analysis methods and results.
A big thank you to Alie and Elana. You both made my time with Cascadia truly
enjoyable. Thank you for all the talks and laughs. And lastly, I want to thank Jack, who
has become a lifelong friend. I am glad we shared a workspace. Thanks for the many
laughs and fun times.
Special thanks to the research team that collected the data that was analyzed in
this thesis: Corliss observers Bethany Diehl, Barry Troutman, Katy Foster, Steven
Jeffries, Kelli Stingle, Annie B. Douglas, Josh Oliver, Alexandra Vanderzee, Kara
Quirke, Corliss captain Lt. Dan O’Hagan.
I would like to express my deepest appreciation to the MES faculty for supporting
me in various capacities. Thank you, Kevin Francis, for being my initial thesis reader and
providing valuable feedback and advice during the initial writing phases. I am extremely
grateful to John Withey, who took over the role as my thesis reader during my final
stages. Your insightful feedback and encouragement pushed me to see this to the end.
Could not have finished this without you. Thank you to Mike Ruth for being an
incredible GIS instructor and being an overall positive role model. You are truly an asset
to the MES program and my time there would not have been the same without you.
To my cohort, thanks for all the shared laughs and frustrations. Nothing builds a
stronger bond than sharing highs and lows of a master’s degree program. I especially
want to thank my dear friend Noelle Lore. You have become one of my greatest and
dearest friends. Thanks for keeping me sane by always being down to hike and hang out.
I truly value all the memories and laughs we shared. I would not have been able to do this
without your love and support. Big thanks to my late-night seminar crew! Those nights
were always so much fun and much needed stress relief!
Special thank you to my mom and dad. Thank you, mom, for always sending me
the best care packages. They helped to keep me sane during my most stressful times.
Thank you, dad, for your support and encouragement throughout this entire process.
Would not be here without you both.

viii

ix

: Introduction
Humpback whales are highly mobile marine mammals, migrating seasonally and
traveling up to 10,000 km per year (Baker et al., 1990). Humpbacks were once abundant
throughout the Pacific, Atlantic, Indian, and Arctic Oceans, but commercial whaling
drastically reduced their populations. In the North Pacific (NP), it is estimated that
humpbacks were reduced to 10% of their historic population size by commercial whaling
(Teerlink et al., 2015).
Currently, humpbacks are separated globally into 14 distinct population segments
(DPS), as defined by the National Marine Fisheries Service (NMFS) under the U.S.
Endangered Species Act (ESA). Each DPS has their own conservation status under the
ESA. Their breeding and feeding areas, as well as their migratory routes, often position
them in close proximity to humans. Inhabiting coastal waters, whether for feeding or
breeding, puts them at an increased risk of ship strikes, risk of negative impacts from
noise pollution, and potential for entanglement in fishing gear. Also, habitat degradation
through coastal development and over-fishing can cause a decrease in prey availability.
Recently, it has been observed that microplastics are increasingly becoming a serious
threat to humpbacks as well (Besseling et al., 2015).
Abundance and density estimates of humpback whales aids in the identification of
seasonal high-density areas as well as detecting changes in the population over time. The
continual update of these estimates is crucial for creating and implementing effective
conservation management strategies. A common method of obtaining theses estimates is
through the conduction of systematic line-transect surveys, which allows for the

1

collection of sighting data that is used to assess status, detect trends, and predict habitat
use (Barlow, 2010; Barlow & Forney, 2007; Rone et al., 2017).
For the outer coast of Washington State (WA) and Oregon (OR), abundance
estimates are necessary to implement effective regulation and management strategies to
mitigate the negative effects of anthropogenic activities on their populations. Here,
humpbacks are highly susceptible to entanglement in crab pots nearshore and along the
coasts (Carretta et al., 2016). The shipping channels entering the Strait of Juan de Fuca
and the mouth of the Columbia River are a major site for cetacean vessel collisions due to
the amount of shipping traffic they experience throughout the year (Douglas et al., 2008).
The Strait of Juan de Fuca is suggested to be an area of especially high-risk for ship
strikes because it is the only entrance into the Puget Sound for vessels, creating a
‘bottleneck’ area for whales and ships (Williams & O’Hara, 2010). There is similar
concern for vessel strikes of large whales at the mouth of the Columbia River on the
border of Washington and Oregon. This, coupled with expected increase of vessel traffic
as amount of exported cargo increases, can make humpbacks traveling near coasts of WA
and OR highly susceptible to fatal ship strikes (BST Associates, 2017).
Accurate abundance estimates are extremely important, making it necessary to
analyze new line-transect data to ensure policy decisions are being made on the most
current data available. According to NMFS, abundance estimates are considered outdated
after eight years (NMFS, 2005). Analyzing line-transect survey data in a timely manner
should be of the utmost importance because it allows for performance of rigorous
statistical analyses to keep these estimates as current as possible. While there are citizen

2

science data available on presence of whales (i.e., whale watch tours), this data is
unstructured and tends to be biased towards where people are (Kamp et al., 2016).
Density and abundance estimates are significant because they can be used to
understand and potentially predict human impacts on cetaceans. Unfortunately, it is not
possible to fully understand the negative impact of anthropogenic activities on humpback
whale populations as there is no way to track every death attributed to human activities.
Therefore, abundance estimates provide our best insight into these impacts and can be
used by a multitude of agencies to develop more effective conservation management
practices. The U.S. Navy, for example, utilizes cetacean abundance estimates to identify
potential impacts their training can have on specific species (U.S. Department of the
Navy, 2015). Meanwhile, Feist et al. (2015) used abundance estimates to quantify
impacts of fishing fleets on cetaceans in the California Current. Similarly, density and
abundance estimates have been used in determining high risk area of cetacean-vessel
collisions (Rockwood et al., 2017).
Surveys have been carried out periodically to estimate cetacean abundances along
the US West Coast (Barlow, 1997; Barlow, 2003; Calambokidis & Barlow, 2004;
Calambokidis et al., 2004; Zerbini et al., 2007; Moore & Barlow, 2015), however, these
studies focused on obtaining yearly estimates. This study used methods to determine
seasonal and yearly distribution of humpbacks along Washington and Oregon coasts from
surveys conducted between 2011 and 2012. Results presented here provide a novel look
at seasonal abundance and density of humpbacks in this area, which is valuable when
creating conservation management action plans.

3

: Literature Review
This literature review will explore the historical and current conservation status of
humpback whales in the North Pacific Ocean, issues related to their overall recovery, the
use of systematic line-transect surveys in conjunction with distance sampling to generate
abundance and density estimates, and the importance of these estimates for effective
conservation management strategies. The review will start with a general overview of
humpback whales in the North Pacific, including information about their biology, range,
and taxonomy. The review will next cover the history of their conservation status, to
include their recent separation into distinct population segments globally. It will then
synthesize current anthropogenic threats to their populations, followed by an examination
of one of the most popular methods for collecting data to generate abundance and density
estimates -- line transect surveys.

2.1 Humpback Whales (Megaptera novaeangliae)
2.1.1 Evolutionary History
Cetaceans are an order of mammals that originated during the Eocene epoch about
fifty million years ago (MYA; Thewissen et al., 2009). Cetaceans are comprised of
whales, dolphins, and porpoises. Fossil records, morphological, and molecular evidence
support the hypothesis that cetaceans evolved from land mammals (Luo, 2000). After
becoming fully aquatic, their evolutionary diversification accelerated.
The evolutionary history of cetaceans is one of the best-understood examples of
macro-evolutionary change (Bajpai et al., 2009). Currently, the most widely supported
hypothesis is the cetacean-artiodactyl hypothesis, due to recent findings that cetaceans
4

and artiodactyls share specific ankle bones called astralagi (Thewissen and Madar, 1999;
Thewissen et al. 2009). It is suggested that the closest extant artiodactyl relative of
cetaceans are the hippopotomids (Gingerich et al., 2001). Analyses of the fossil record
have discovered five families from the Eocene, believed to have led to the modern-day
cetaceans: Raoellidae, Pakicetidae, Ambulocetidae, Remingtonocetidae, Protocetidae,
and Basilosauridae (Figure 2.1; Bebej, 2011). Neocetes are considered to be modern day
whales that consist of two suborders, Odonotceti and Mysticeti.

Family: Raoellidae

Family: Pakicetidae

Family: Ambulocetidae

Family: Remingtoncetidae

Family: Protocetidae

Family: Basilosauridae

Figure 2.1: Evogram detailing evolutionary history and important morphological
adaptions of whales dating back 55 MYA. (Image adapted from Zimmerman, C.
(2009). The Tangled Bank: An Introduction To Evolution. Roberts and Company
Publishers.)

5

2.1.1.1 Morphological Adaptions
Cetaceans have gone through extreme morphological adaptations to become
giants of the sea. Starting in the Early Eocene, the osterosclerotic cortex (a thickening and
hardening of specific bones) was found in cetaceans’ closest extinct relative from the
genus Indohyus of the Raoellidae family. The finding of osterosclerotic limb bones in this
family has been suggested as a critical piece in understanding cetacean evolution from
terrestrial life to aquatic (Cooper et al., 2012). The function of osteosclerosis is to
counteract buoyancy and allow for stability while wading in the water. It was also found
in Pakicetids and Ambulocetids, two families of extinct cetacean ancestors (Bajpai et al.,
2009). This adaptation permitted late cetaceans to spend more time in the water and less
time on land, initiating the gradual transition of a mainly terrestrial life to a fully aquatic
life.
As late cetaceans spent an increased amount of time in the water, more
adaptations are found in their fossils that supported suitability to aquatic life. The large
size of the bulla found in Pakicetid fossils from the Early Eocene is interpreted as an
adaptation for underwater hearing and since has been a characteristic found in all
cetaceans (Uhen, 2007). Modern day cetaceans have a pad of fat in their lower jaw that is
connected to the middle ear, allowing the transfer of underwater sounds (Thewissen et al
2009). This was present in Remingtoncetids (a fully aquatic family), suggesting that
underwater sound transmission was an important aquatic adaptation (Thewissen et al.,
2009). These adaptations have helped cetaceans hear directionally underwater, thus
increasing underwater survivability (Uhen, 2007).

6

Studies of the Ambulocetids have significantly contributed to our understanding
of the evolution of cetacean locomotion (Madar et al., 2002). They demonstrate a means
of locomotion that lies between a land mammal and a modern whale, and provides the
link between cetaceans’ terrestrial ancestors to modern cetaceans (Thewissen and Bajpai,
2001). Based on the morphology of their hind limbs and tail, their means of locomotion
was a mix between pelvic paddlers and caudal undulators (Thewissen and Bajpai, 2001).
They had hip flexors, extensors, and adductors, which are important for both walking on
land and stabilization in the water (Madar et al., 2002). Following the Ambulocetids,
Remingtoncetid morphology shows movement by caudal locomotion, indicated by the
location of the adductor muscles of the thigh (Bajpai et al., 2009). In Protocetids we start
to see a decrease in limb size, indicating slower land locomotion due to decreased ability
to support their weight (Bajpai and Thewissen, 2001). Their swimming style also changes
to a combination of hind limb paddling and dorsoventral undulations of the tail
(Thewissen et al., 2009). With the Basilosaurids, we see the emergence of a fluke and
caudal swimming (Bajpai et al., 2009).
A change in the position of the eye orbits is first seen in the Ambulocetids,
moving towards the side and higher up on the skull resembling those of modern-day
hippopotamus (Thewissen et al., 2009). The eyes of Protocetids become large, face
laterally, and are set farther from the midline of the skull under a supraorbital shield
(Thewissen et al., 2009). Modern cetaceans today have eye orbits placed widely apart
under broad supraorbital processes (Fordyce and Barnes, 1994). Starting with the
Protocetids, the nasal opening has started to move further posterior on the snout, starting
the formation of the blowhole we see in modern day cetaceans (Thewissen et al., 2009).

7

The blowhole is an important adaptation because it allows for breathing while submerged
in water (Thewissen et al., 2009). In Basilosaurids, the nasal opening has shifted far back
on the snout toward the eyes and is now considered a blowhole (Thewissen et al., 2009).
For Protocetids the emergence of osmoregulation of saltwater appears through stable
isotope records taken from fossils (Clementz et al., 2006). With the ability to
osmoregulate saltwater, Protocetids gain the ability to travel across the open marine
waters and increase their geographic distribution to areas including the Pacific and
Atlantic Oceans (Clementz et al., 2006).

2.1.1.2 Cetaceans Today
There are currently two different extant suborders of cetaceans that originated
from Basilosaurids near the Eocene and Oligocene boundary that took off and spread all
over the globe: Odontoceti and Mysticeti (Uhen, 2010). Mysticetes are distinguished by
baleen plates and a dual opening in their blowhole, while Odontocetes are distinguished
by their distinct teeth, ability to echolocate, and a single opening in their blowhole (Uhen,
2007). The Odontocetes are composed of many different subfamilies that include
dolphins, pilot whales, melon-headed whales, and narwhales. Known as the toothed
whales, they developed echolocation to locate prey to make up for their loss of their sense
of smell (Gatesy et al., 2012). Odontocetes have a unique mechanism to locate their prey
that is not found in any other animal (Mckenna et al., 2012). They have an organ called
the melon that is located at the front of their skull that produces the sound energy used to
echolocate prey (Mckenna et al., 2012). Echolocation appeared around the Early
Oligocene as a result of changing food resources, changing oceans, and continental
rearrangement (Fordyce and Barnes, 1994). All fossils have been found with the
8

structures used in modern cetaceans for echolocation and both is past and the present
been widely used in navigation and hunting (Fordyce, 2003).
The Mysticetes are composed of subfamilies that include humpback whales, grey
whales, blue whales, and right whales. Mysticetes have baleen plates instead of teeth and
have retained their sense of smell (Gatesy et al., 2012). Baleen is a keratinous strainer
that enables filter feeding which is an important that enables the intake of huge energy
resources (Demere et al., 2007). Filter feeding can be seen in majority of the fossils of
Mysticetes (Fordyce and Barnes, 1994). It has been found that early Mysticetes were
toothed but that these teeth had the basic genetic structure for baleen (Demere et al.,
2007). A nutrient of baleen was present in animals from the late Oligocene that led to the
present-day baleen (Demere et al., 2007). The evolution from teeth to baleen may have
emerged from its increased efficiency to capture prey (Demere et al., 2007).

2.1.2 Species Description
Humpback whales
(Megaptera novaeangliae), like
other whales, dolphins, and
porpoises, are part of the order

Box 1. Taxonomic Classification
Order:
Suborder:
Family:
Genus:
Species:

Cetacea
Mysticeti
Balaenopteridae
Megaptera
Megaptera novaeangliae

all

Cetacea (Box 1). Humpbacks are classified under the suborder Mysticeti, which
represents the baleen whales that rely on filter feeding using baleen plates instead of
teeth. Megaptera, their scientific name meaning “large-winged”, comes from their long,
wing-like flippers that can measure up to one-third of their body length. Humpback
dorsal body coloration is black, and their underside pigmentation varies between black,
white, or mottled (Error! Reference source not found., Clapham, 2018). Flipper dorsal c
9

oloration varies depending on population and individual (i.e., North Atlantic populations
tend to be white and North Pacific populations tend to be black) (Clapham, 2018). Like
all whales in their family, they have pleats that run along their jaw, but a distinguishing
feature of this species are tubercles (knobs) that cover their head and jaw (Clapham,
2018). Their flukes also have features unique to this species that are used for
identification of individuals within populations. Fluke ventral coloration patterns range
from all white to all black and the fluke trailing edge is prominently serrated (Clapham,
2018). Adults range in size from 14 – 17 m. It is not easy to discern between males and
females, but females are reported to be about 1 – 1.5 m longer than males and they have a
lobe the size of a grapefruit towards rear of their genital slit (Clapham, 2018).
Humpbacks are considered generalists when it comes to feeding, preying on
euphausids and small schooling fish (Clapham, 2018). A unique feeding behavior they
display is bubble netting, the act of trapping schooling fish in a net of bubbles near the
surface and then lunge feeding into the center of the trapped fish (Clapham, 2018). It is
unclear how humpbacks find their food, but it is suggested that they rely on their sense of
smell. Socially, they form short-term groups associated with feeding and breeding, with
long term associations only occasionally recorded. It is believed that the main purpose of
males’ long and complex songs is to attract females but could also be used to assert
dominance or initiate cooperative behavior (Clapham, 2018). Humpbacks have been
dubbed as charismatic megafauna due to their tendencies to display spectacular aerial
behavior, such as breaching and lobtailing.

10

Figure 2.2: Northern Hemisphere (top) and Northern Hemisphere (top) and Southern
Hemisphere Humpback whale. (Image adapted from Clapham, P. J. (2018). Humpback
whale: Megaptera novaeangliae. In Encyclopedia of marine mammals (pp. 489-492),
Academic Press.)

11

2.2 Policies, Conservation Status, and Management
Humpbacks can be found inhabiting all major oceans around the world. Being
seasonal migrators that can travel over 10,000 km a year, no surprise this species has
extensive ranges (Baker et al., 1990). In the North Pacific (NP), their winter breeding
areas extend throughout the western NP Ocean, the Hawai’ian Islands, and along coasts
of Mexico down through Central America (Error! Reference source not found.; C
alambokidis et al., 2015). During spring and summer months, they migrate along coastal
feeding areas, ranging from California, Alaska, and Russia (Calambokidis et al., 2015).
Genetic and photo-identification data indicate that humpback whales have strong
site fidelity towards specific feeding and breeding areas, with six feeding and six
breeding areas identified in the NP (Barlow et al., 2011; Calambokidis et al., 2015). The
National Marine Fisheries Service (NMFS) has divided the global population of
humpbacks into 14 distinct population segments (DPS) based on breeding areas, with
each DPS having their own conservation status under the Endangered Species Act (ESA)
(Error! Reference source not found.; 81 FR 62259; NOAA, 2016a). Under the Marine M
ammal Protection Act (MMPA), there are four identified humpback whale stocks based
on breeding areas, each assigned their own protection status independent of the ESA (16
U.S.C. §§ 1361 et seq.).

12

Figure 2.3: Identification, location, and current conservation status of the 14 identified distinct
population segments of humpback whales (numbered circles) and their associated feeding areas
(green
circles).
(Image
adapted
from
NOAA
Fisheries
https://www.fisheries.noaa.gov/species/humpback-whale).

2.2.1 Policy and Conservation Status
There are numerous acts and policies dedicated to directly protecting marine
mammals within the U.S. implemented by various federal, state, and native tribe entities.
Their main sources of protection nationally come from the MMPA and ESA, and
internationally through the Convention on International Trade in Endangered Species of
Wild Fauna and Flora (CITES) and the International Whaling Commission (IWC). Under
the MMPA, marine mammals are protected through prohibition of hunting, harassing,
capturing, or killing any marine mammal species in U.S. waters as well as the prohibition
of importing or exporting marine mammals and their parts as products (16 U.S.C. §§
1361 et seq.) Humpbacks were listed as an endangered species under the ESA in 1973.
They were also classified as ‘endangered’ on IUCN’s Red List in 1988. Marine mammal
species that are given a conservation status of ‘endangered’ or ‘threatened’ are allocated
13

additional protection under the ESA (16 U.S.C. § 1531 et seq.). MMPA provides marine
mammal species protections regardless of conservation status under ESA. CITES and
IWC provide avenues for international collaboration to protect and conserve marine
mammal species.
Their population has increased since the world-wide embargo on commercial
whaling established by the International Whaling Commission (IWC) in 1966 and
protections under the MMPA and ESA (Fleming & Jackson, 2011; Wedekin et al., 2017).
As of 2008, humpbacks are classified as ‘least concern’ on IUCN’s Red List (Reilly et
al., 2008). The IUCN serves to classify species at high risk of global extinction and does
not provide any management recommendations. They currently have nine categories,
ranging from extinct to least concerned, a taxon can be classified into based on a species
extinction risk (IUCN Standards and Petitions Committee, 2019). To determine if a
species falls under a threatened category, they are assessed by a panel of experts based on
five criteria: population decline measured longer than 10 years, reduction in geographic
range occurrence and/or occupancy, abundance, and quantitative estimates of the direct
risk of extinction (IUCN Standards and Petitions Committee, 2019). A species’ listing is
based on the highest category of threat within any given criteria. According to IUCN,
humpbacks were down-listed because of their overall increasing population numbers and
population reaching 60,000 individuals world-wide, excluding them from criteria that
would qualify for a higher classification of concern.

2.2.1.1 Marine Mammal Protection Act (MMPA)
The MMPA was enacted on October 21, 1972 in response to Congress identifying
that marine mammals have economic, recreational, international, and ecological
14

significance, realizing that certain species and population stocks are at risk of extinction
due anthropogenic activities and require federal protections to prevent loss of species (16
U.S.C. §§ 1361 et seq.). The main purpose of the MMPA is to provide policies of
resource management with the primary objective to maintain healthy and stable marine
ecosystems to prevent unsustainable species population decline (16 U.S.C. §§ 1361 et
seq.). It is implemented by the National Marine Fisheries Service (NMFS), U.S. Fish and
Wildlife Service (FWS), and the Marine Mammal Commission. MMPA defines ‘take’
similarly to the ESA, to mean to harass, hunt, capture, kill, or any attempts thereof to any
marine mammal. Its fundamental objectives are to maintain marine mammal stocks at
optimal sustainable populations (OSP) and as functioning elements of their ecosystems.
Under the MMPA, humpback whales are categorized into stocks based on
geographic location within U.S. waters and EEZ. Along the U.S. west coast there are four
stocks: 1) American Samoa (Hawai’ian Islands area), 2) Western North Pacific, 3)
California/Oregon/Washington, and 4) Western North Pacific. All west coast stocks are
classified as deleted except for the American Samoa stock.
Title I of the MMPA covers the conservation and protection of marine mammals,
providing specifics on taking, importing, prohibitions, exceptions, penalties, regulations,
enforcement, cooperation with states, and conservation plans. Title 1 of the MMPA is
extensive and beyond the scope of this literature review. Of relevance, section 101 of
Title I of the MMPA explicitly prohibits the taking and importing of marine mammals
and their products, with exceptions for scientific research, public display, photography
for educational or commercial purposes, or enhancing survival of a species or stock.
Important specific exceptions are the permitted and authorized incidental takes by

15

commercial fishery industries, oil and gas development, military activities, renewable
energy projects, construction projects, and research (16 U.S.C. §§ 1361 et seq.). All
exceptions require permits before taking or importing any marine mammal.
Title II describes the establishment of the Marine Mammal Commission. The
commission is composed of three members chosen by the President of the U.S. who
exhibit knowledge of the field of marine ecology and resource management (16 U.S.C.
§§ 1361 et seq.). The commission is responsible for providing “independent, sciencebased oversight of domestic and international policies and actions of federal agencies
addressing human impacts on marine mammals and their ecosystems.” Title III describes
the purpose of the International Dolphin Conservation Program. The main goal of this
program is to reduce the amount of dolphin and marine mammal mortalities from
intentional encirclement in tuna purse sein fisheries (16 U.S.C. §§ 1361 et seq.). Title IV
establishes the purpose of marine mammal health and stranding response program. It also
provides guidelines for the collection and distribution of data relating to health of marine
mammals and coordinate effective responses to unusual mortality events (16 U.S.C. §§
1361 et seq.). And finally, Title V deals specifically with polar bears (Ursus maritimus)
and their conservation and protections.
Overall, the MMPA serves to provide rules and regulations to prevent marine
mammal populations from depleting due to anthropogenic activities. It requires NMFS
and FWS to prepare yearly stock assessments of marine mammals that occur in US
waters and the Exclusive Economic Zone (EEZ). These assessments must contain
information on stock distribution and abundance, population growth rates and trends,
estimate annual deaths from anthropogenic activities, known fishery interactions, and

16

stock status. Stocks are assigned a status of strategic or non-strategic based on their
listing under the ESA, if the number of human-caused deaths exceeds their estimated
PBR, and if the population is declining and will be listed under the ESA in the near future
(Carretta et al., 2019).

2.2.1.2 Endangered Species Act (ESA)
The main purpose of the ESA is to protect and recover threatened and endangered
species along with the ecosystems they depend on. Humpback whales were listed
globally as an endangered species under the ESA from 1970 - 2016 (Fleming & Jackson,
2011). Currently, the smallest taxonomic unit identified under the ESA is subspecies
(Rosel et al., 2017b). A distinct population segment is a grouping of individuals within a
species that differ from other individuals in the same species; however, these differences
are not significant enough to warrant separation of this grouping into a full subspecies.
Designation of a species into DPS is determined based on criteria established by the FWS
and NMFS. First, either a population segment must be considered discrete from other
groups, being bound by a physical barrier that does not allow interaction between
populations, or noticeably distinct based on physical factors such as different
pigmentation, physiological factors such as differences in bodily functions, or ecological
factors such as feeding/breeding area preferences (16 U.S.C. 1531 et seq.). For this,
measures of genetics can be used to provide supporting evidence of these differences.
Second, loss of the population would create a significant gap in the species’ range,
(Grunwald et al., 2008).
The NMFS and National Oceanic and Atmospheric Administration (NOAA)
began a status review for humpbacks in 2009. Reviewing petitions from the Hawaii
17

Fishermen’s Alliance for Conservation and Tradition, Inc. and State of Alaska ultimately
led to the division of humpback whales into distinct population segments (DPS) (50 CFR
224). The purpose of separating individuals into these distinct population segments is to
allow FWS and NMFS “to protect and conserve species and the ecosystems upon which
they depend before large-scale decline occurs that would necessitate listing a species or
subspecies throughout its entire range” (16 U.S.C. 1531 et seq., p. 4725). The theory is
conserving and protecting DPS is more cost and time effective than conserving a whole
species within its entire range (16 U.S.C. 1531 et seq.).
Humpback DPS identification was based on extensive genetic studies that
demonstrate humpbacks have strong site fidelity to specific feeding and breeding areas,
with rare instances of intermingling (Calambokidis et al., 2008; Barlow et al., 2011;
Baker et al., 2013; Calambokidis et al., 2015). In the NP, Mexico is listed as threatened,
and Western North Pacific and Central America are listed as endangered (Figure 2.3; 16
U.S.C. 1531 et seq.). With the designation of endangered or threatened, protective
measures that go beyond those established by the MMPA are provided to each DPS.
The implementation of the ESA for marine mammals falls under the
responsibility of NOAA Fisheries. Marine mammals that are identified as endangered or
threatened have similar protections as depleted stocks under the MMPA. NOAA
Fisheries is required to carry out various management actions for species listed under the
ESA including, but not limited to, designate critical habitat, monitor and evaluate species
status, and develop and implement recovery plans (16 U.S.C. 1531 et seq.).

18

2.2.2 Management
The main goal of management plans is to prevent populations from depleting, but
it is a challenge to predict how severe of an impact anthropogenic threats will have on
populations. Universally, there are several methods used to assess/manage whale
populations in efforts to prevent populations from going extinct including IUCN Red List
Criteria, European Union’s (EU) Habitats Directive, US Marine Mammal Protection Act
(MMPA) & Potential Biological Removal (PBR), and International Whaling
Commission’s (IWC) Revised Management Procedure. The EU’s Habitats Directive aims
to conserve rare, threatened, and endemic species as well as rare and characteristic habitat
types, encompassing over 1000 animal and plant species and 200 habitat types (Council
of the European Commission, 1992). Member states of the EU are required to enforce
laws, regulations, and administrative provisions needed to comply with the Directive. The
Revised Management Procedure developed by IWC, serves to estimate sustainable catch
limits for commercial whaling of baleen whales by maintaining populations at 72% of
their carrying capacity and prohibits commercial whaling of populations who are below
54% of their pre-exploitation levels (IWC, 1994). In the US, an animal’s potential
biological removal is used to determine the amount of animals that can be removed from
a population, excluding natural deaths, without having a negative impact on the species
survival.

2.2.2.1 Potential Biological Removal
The MMPA and ESA require the investigation of population structure, estimate
population size and trends in abundance, identify and mitigate anthropogenic threats, and
designate critical habitat to maintain populations at optimum sustainable population
19

levels to prevent the extinction of species. According to the NMFS, a population is
considered depleted if they are estimated to be 50-70% below their historic population
size (Wade, 1998). Determining if a population is declining to 50-70% below their
historic population size requires a great deal of monitoring encompassing many years to
determine abundance trends. Often times, by the time a declining trend is detected, it is
too late, and the population is already at “depleted” levels (Wade, 1998). A robust
management strategy takes into account precision and bias of estimated abundance and
mortality and the uncertainty of population growth rate (Wade, 1998). If the source of
mortality is known, the level of human caused mortality can be estimated (Wade, 1998).
The Potential Biological Removal (PBR) level is the number of animals that can
be removed every year from a stock due to reasons other than natural mortalities while
allowing the stock to maintain optimal sustainable population levels (NOAA, 2018). It is
calculated by the following equation:
PBR = Nmin * ½ Rmax * Fr
Where:
Nmin = minimum population estimate = 20th percentile of a log-normal
distribution of the population abundance estimate
Rmax = the maximum theoretical or estimated net productivity rate of the stock
at a small population level. The default values are 0.04 for cetaceans and 0.12
for seals.
Fr = a recovery factor that is between 0.1 and 1. The default values are 0.1 for
endangered stocks and 0.5 for depleted and threatened stocks and stocks of
unknown status.

Generally, the maximum growth rate and total population size are near impossible
to be measured directly, so approximations of these variables from readily available data
is used when estimating PBR. It is easy to implement when assessing the impact for
situations where mortalities are directly observed, like bycatch in fisheries, but

20

challenging when cause of death is unknown or for instances that just increase the risk of
mortality, such as microplastic ingestion or noise pollution (Lonergan, 2011). Despite its
limitations, PBR is used to gauge how much of an impact anthropogenic activity are
having on populations. If certain anthropogenic activities are causing more mortalities
than the PBR allows, that helps to focus where management efforts should be targeted.
Currently, the PBR for the California/Oregon/Washington stock of humpback whales as
identified by MMPA within U.S. is calculated to be 16.7 whales per year, meaning that
16.7 whales can die due to anthropogenic activities each year without having a negative
impact on their population (Carretta et al., 2019).

2.3 Monitoring Methods
The effective conservation and management of whales, including humpbacks,
relies on rigorous and sound scientific research. The scope and amount of funding
typically dictates which monitoring method can be implemented. Whales can be difficult
to locate and track throughout the world’s oceans, so a variety of research technologies
and techniques are used to study their movements, behaviors, and population trends.
Most popular methods include passive acoustic monitoring, satellite tagging, photo
identification, and vessel-based line-transect surveys. Often, multiple techniques and
methods are used together. Data acquired from marine mammal monitoring projects are
then used in calculating abundance and density for populations in each area.
Passive acoustic monitoring utilizes sounds produced by whales to understand
migration and distribution patterns, acoustic behavior and movement, and in conjunction
with visual survey, deriving abundance and density estimates (Stanistreet et al., 2013;
Risch et al., 2014; Davis et al., 2017). Satellite tagging of whales has provided insight
21

into whale movement and behaviors, shedding light on migration routes and feeding
locations and behavior with the addition of time-depth sensors (Kennedy et al, 2013;
Owen et al., 2015; Cerchio et al., 2016). Whales can be identified based on distinct body
markings on either underside of the fluke or markings on dorsal fin area. Long term
tracking of individuals with photo-ID has shed light on migratory patterns and habitat
usage (Stevick et al., 2004; Gabriele et al., 2017). The most commonly used method to
study whales is through vessel based systematic line-transect surveys within an
established study area. These methods often follow standardized distance sampling
protocols, with resulting data used to generate abundance and density estimates.

2.3.1 Distance Sampling
Distance sampling was historically referred to as “line transect sampling”
(Burham et al, 1980). Now known as Distance sampling, it is the mostly widely used
technique for estimating abundances of wild animal populations (Buckland et al., 2004)
Distance sampling is a group of methods that are widely used to estimate abundances of
biological populations, by providing a rigorous framework for estimating detectability
(Burnham et al., 1980; Buckland et al., 2004, 2015; Thomas et al., 2010, 2014). Distance
sampling allows for estimation of abundance in an area without needing to count every
animal within the area of interest. Standardized methods are detailed in Buckland et al.
(2001). Distance sampling blends together model-based and design-based statistical
methods, using the modeled detectability of surveyed transects or plots estimate
abundance of animals outside the surveyed area (Buckland et al., 2004). Distance
sampling helps to guide the placement of transects to cover a proportion of the study area,

22

which allows for the estimation of detection probability. In this way, one can estimate
abundance and density without having to count every animal.
Accurate abundance and density estimates rely on obtaining exact numbers of
animals within a certain area. This is achieved by plot, quadrat, or strip sampling methods
where all animals of interest are counted in an established plot, quadrate, or strip
(Burnham et al., 1980). With these methods, random strips or plots with a certain area, a,
are assigned within a large survey area of size A, and all animals of interest are counted,
n, along the strips (transects) or in the plot (Marques, 2009). We can then calculate the
density, or number of animals per unit area, as well as estimate animal abundance for the
entire survey area because it is assumed that all animals that are in the sampling plots or
strips are counted (Marques, 2009).

Density is calculated by the formula:
𝐷=

𝑛
𝑎′

And abundance by the formula:
̅̅̅̅
𝑁𝑎 = 𝐴

𝑛
𝑎′

These standard methods of estimating abundance and density are difficult to
implement when studying wildlife populations because not all animals are counted. When
studying whales, it is especially difficult to count them all because they spend majority of
their time below the surface of the water. This will result in inaccurate and impractical
estimates. To improve accuracy of estimates, we need to account for the proportion of
whales missed during surveys.

23

2.3.1.1 Distance Practices
For vessel-based line transect distance sampling, a vessel navigates along
systematically spaced transect lines with a random starting point (Thomas et al., 2010).
Observers perform a standardized survey while the vessel follows a linear transect
searching for animals or clusters of animals (Thomas et al., 2010, 2014). When an
animal, or cluster of animals, is detected, the distance that the animal is from the line is
recorded (Figure 2.4). A major assumption of distance sampling is that all animals on the
transect line are detected (Thomas et al., 2010, 2014). It is expected that objects become
harder to detect the farther away they are from the line, thus observations decrease with
increasing distance (Thomas et al., 2010, 2014). The distribution of recorded distances is
used to estimate the proportion of animals missed during the survey (Buckland et al.,
2015; Thomas et al., 2010, 2014). From here, abundance and density estimates of animals
can be obtained for the survey area (Thomas et al., 2010, 2014). Distance sampling has
three assumptions: 1) objects on the line are detected with certainty, 2) objects do not
move, and 3) measurements are exact (Thomas et al., 2010).

24

Figure 2.4: Measurements that are recorded during line transect surveys.
An area size A is sampled by following along a line of length L. An
objected is detected at distance r from observer and sighting angle θ is
measured to calculate perpendicular distance x. The distance the object is
from observer parallel to transect at detection is z = r * cos (θ). (Image
adapted from: Buckland, S.T., Anderson, D.R., Burnham, K.P. and Laake,
J.L. (1993) Distance Sampling: Estimating Abundance of Biological
Populations. Chapman and Hall, London. 446 pp.).

2.3.1.2 Distance: Abundance and Density Calculation
The key component in obtaining abundance and density in distance sampling is
the estimation of a detection function. The detection function describes the relationship
between distance and probability of detection (Buckland et al., 2001). A major
assumption in distance analyses is that the detection function (g(y)) at distance 0 (y=0) is
100%: g(0) = 1 (Buckland et al., 2001). In many instances, especially in cetacean
research, g(0) < 1, due to availability bias (failure to detect an animal due to diving) and
perception bias (observers failing to detect animals that are at the surface) (Pollock et al.,
2006). Various methods can be implemented to reduce these biases but require more
effort, time, and personnel (Buckland and Turnock, 1992; Laake et al., 1997; Hiby and
25

Lovel, 1998). Distance rescales the detection function g(x) to integrate unity so that we
are now estimating the probability density function, 𝑓̂(0), of perpendicular distances to
detected objects (Thomas et al., 2002). The probability density function (pdf), 𝑓̂(0),
describes the relationship between distance and probability of detection (Buckland et al.,
2001). The following formulas are used to estimate density and abundance from data
collected from Distance sampling:
̂=
Density: 𝐷

𝑛𝑓̂ (0)
2𝐿

̂=
Abundance: 𝑁

𝑛𝑓̂ (0)𝐴
2𝐿

Where:
n = number of animals or clusters detected
L = total length of transects surveyed
A = size of survey region
𝑓̂(0) = f(x) evaluated at 0 distance; f(x) = probability density function (pdf) of
observed distances

The surveys provide us values for n, L, and A, and Distance will estimate 𝑓̂(0)
through fitting parametric ‘key’ functions onto histogram of recorded perpendicular
distances of sightings (Thomas et al., 2002). Each ‘key’ function uses a different formula
to estimate 𝑓̂(0) ((Buckland et al., 2015). Distance assigns each model an Akaike
Information Criterion (AIC) value based on how well the key function fits the data
(Akaike, 1973). The model with the lowest AIC value is determined to fit the histogram
of perpendicular distances the best and use that model’s estimated 𝑓̂(0) value into the
abundance and density formulas to give estimates.
This can be done by using one of three different analysis engines in the software
Distance: 1) conventional distance sampling (CDS), 2) multiple covariate distance
sampling (MCDS), and 3) mark-recapture distance sampling (MRDS). This thesis used
CDS and MCDS to generate abundance and density estimates. CDS operates under the
26

assumption that detection probability of an animal only decreases with increasing
perpendicular distance from the transect line (Buckland et al., 2004). MCDS assumes the
detection function is influenced by a number of factors, or covariates, other than distance
(Buckland et al., 2004). MCDS allows the inclusion of various covariates when
estimating detection function (Buckland et al., 2004). MCDS can potentially yield more
efficient estimates of abundance, depending on whether exploratory analyses indicate
something other than distance is influencing the detection probability.

2.4 Line-Transect Surveys and Abundance and Density
Effective conservation management relies on the continual update of species
abundance estimates. These population estimates are vital for determining seasonal highdensity areas and detecting changes in the population in order to implement more
effective conservation management strategies. Results from line-transect surveys utilizing
distance sampling protocols have been frequently used to generate density and abundance
estimates. New surveys and analyses are needed to understand population fluctuation,
which in turn drive better understanding and management. Systematic line-transect
surveys provide information on whale species, allowing us to assess status, detect trends,
and predict habitat use (Rone et al., 2017).
Previous studies have utilized line-transect surveys following distance sampling
protocols (LTS) to establish population abundance estimates and aid in conservation of
numerous animals, especially in whale research. Studies by Rone et al (2017), Barlow &
Moore (2017), and Bradford et al. (2017) employed LTS in their research. Rone et al.
(2017) conducted three LTS in offshore waters of the Gulf of Alaska, a region previously
unsurveyed due to various environmental factors, to determine baseline density,
27

distribution, and abundance estimates of six species of cetaceans. Other studies have used
LTS to update trend abundance estimates for various species of cetaceans including
beaked and sperm whales (Bradford et al., 2016; Barlow & Moore, 2017).
A popular method of analyzing LTS data to obtain abundance and density
estimates is through the creation of habitat-based density models, a type of species
distribution model (SDM) (Figure 2.5; Becker et al., 2017; Roberts et al., 2016; Forney
et al., 2015; Forney et al., 2012) For example, Becker et al. (2017) used data from 20
LTS to develop seasonally-explicit habitat-based density models for three whale species
in the California Current. Also, Forney et al. (2012) developed habitat-based density
models that displayed species density maps for 22 whale and dolphin species from 15
LTS in the temperate and tropical eastern Pacific Ocean. Analyses are also performed to
update existing habitat-based density models. Forney et al. (2015) updated habitat-based
density models for whale and dolphin densities around Hawai’i and other central pacific
islands from 15 LTS. From these updated estimates, they were able to produce high-use
areas for each of the ten documented species and estimate monthly cetacean abundance
by incorporating satellite-derived environmental data. Such studies have shown the value
of LTS and their accompanying analyses, which is the focus on this thesis.

28

Figure 2.5: Updated model-based densities for false killer whale, short-finned
pilot whale, sperm whale, and Bryde’s whale from line-transect surveys (grey
lines) for years 2002 and 2010 from 15 systematic line-transect ship surveys
conducted by National Oceanic and Atmospheric Administration (NOAA)
Southwest Fisheries Science Center and the Pacific Islands Fisheries Science
Center between 1997 and 2012. Black dots represent locations of sightings.
(Figure adapted from Forney, K. A., Becker, E. A., Foley, D. G., Barlow, J., &
Oleson, E. M. (2015). Habitat-based models of cetacean density and distribution
in the central North Pacific. Endangered Species Research, 27(1), 1-20.)

29

2.5 Current Threats
Humpback whales are susceptible to negative impacts from anthropogenic
activities and having abundance and density estimates allows researchers to monitor and
assess how much of an impact they are having on populations. When large migration
routes overlap shipping channels, then risk for ship strikes and entanglement in fishing
gear increases. Increase in vessel traffic throughout the oceans causes negative impacts
from noise pollution by interfering with communication and breeding. Overfishing by
commercial fisheries is leading to a decrease in prey availability. Climate change is also a
potential threat to humpback whales and many marine species, as it is unclear when and
how available prey will shift and how the apex predators will adapt. Understanding their
current threats is necessary for implementing effective conservation management
strategies.

2.5.1 Ship Strikes
Humpback whales, being slow moving, large marine mammals, are highly
susceptible to ship strikes. Their migration routes and feeding areas often overlap with
shipping lanes, resulting in increases of ship strikes that can lead to serious injury or
death. The National Marine Fisheries Service (NMFS) has established that the humancaused mortality limit (Potential Biological Removal, PBR) is 11 for humpback whales
(Carretta et al., 2017). Rockwood et al. (2017) suggests that the number of whales struck
and killed by cetacean-vessel collisions are greater than previously suspected, and
significantly greater than the established PBR counts. Whale carcasses, in general, tend to
sink before the bodies can be beached, which results in low carcass recovery rates. This
contributes to challenges in quantifying accurate numbers of whale mortalities caused by
30

collisions (Laist et al., 2001). Based on recent models of ship strike effects on cetacean
populations, fatal cetacean-vessel collisions are one of the leading causes of humanrelated death for large cetaceans in the U.S. and around the world (Rockwood et al.,
2017; Redfern et al., 2013; Williams & O’Hara, 2010).
Humpbacks are especially susceptible to ship strikes near the entrance to the Strait
of Juan de Fuca and within the Salish Sea because there is one main entry from the
Pacific Ocean utilized by both cetaceans and vessels. Williams & O’Hara (2010) noticed
a pattern in their modeled results of cetacean-vessel collisions that suggested these
‘bottleneck’ areas had highest relative risk for cetacean-vessel collisions. A report from
Washington State Department of Ecology from 2016 stated that, on average, the daily
commercial vessel traffic density in the Puget Sound is 27 vessels per day. Vessel traffic
in the Salish Sea is expected to increase due to increases in amount of cargo exported
(BST Associates, 2017; Cascadia Research Collective, n.d.). The Northwest Seaport
Alliance is the gateway for marine cargo for the ports of Tacoma and Seattle and claims
that the Pacific Northwest is one of the most trade dependent regions of the US.
According to their September 2019 cargo report, in the last five years grand total
containerized volumes (twenty-foot equivalent units, TEUs) have increased 5.8% and
grand total cargo volume (metric tons) have increased 5.5% (Northwest Seaport Alliance,
2019). Also, their international imports are up by 4.5% and exports are up by 8.5% from
the previous year (Northwest Seaport Alliance, 2019). In 2017, it is estimated that $17
billion worth of goods were exported and it’s estimated that 10.5 million metric tons of
containerized cargo are exported yearly with a worth of $12.4 billion (Northwest Seaport
Alliance, 2019). They are also accepting proposal for cargo operations at Terminal 46 in

31

Seattle to support further marine cargo operations with the intent to increase cargo
volumes (Northwest Seaport Alliance, 2019).
Projected increases in vessel traffic increase the risk of vessel collisions in the
Strait of Juan de Fuca and surrounding waters. This coupled with documented increase
and now stabilization of numbers of humpback whales found seasonally along the US
west coast including Washington (Calambokidis et al., 2004; Calambokidis and Barlow,
2004), there are ever more opportunities for vessels and whales to come into conflict.
Humpback whales have made a dramatic return to the Salish Sea in recent years (Steiger
et al., 2015; Calambokidis et al., 2018; Falcone et al., 2005). Understanding factors that
increase probability of ship strikes and establishing high-risk areas of collisions will be
vital for conservation management practices that want to reduce number of humpback
whale and vessel collisions near the entrance to the Strait of Juan de Fuca and the Salish
Sea.

2.5.1.1 Contributing Factors
Understanding factors that contribute to increased probability of fatal cetaceanvessel collisions in necessary for implementing effective management practices to
mitigate fatal cetacean-vessel collisions. Determining a significant relationship between
seasonal whale abundances and vessel strikes will help in evaluating if current mitigation
efforts would be more beneficial if implemented year-round or having seasonal
restrictions. Knowing the effect sea state has on probability of boat operators and/or
observers to detect cetaceans in the water can be beneficial when developing
management practices to decrease cetacean-vessel collisions. If sea state has a negative
effect on probability of whale detection, then that will decrease a boat operator’s ability
32

to avoid collision. If a boat operator or observer cannot detect a whale, then they cannot
reduce speed and change course to avoid collision. It is important to understand if there
are differences in cetacean-vessel collisions between different age classes of cetaceans,
i.e. are juveniles more prone compared to adults, mothers with calf more prone than those
without. This goes along with seasonal variation. Presence of calves coincides with
seasonal variation so this could strengthen the need for either seasonally restricted or
year-round mitigation efforts. Finally, understanding what effect vessel speed has on the
probability of fatal cetacean-vessel collisions. If we know how speed, along with the
other stated factors, affects probability of fatal collisions, it will help in analyzing overall
how effective current management practices are for mitigating cetacean-vessel collisions
in the Salish Sea.

2.5.1.2 Seasonal Variation
Humpbacks traveling along the west coast of North America have wide migratory
ranges—from waters around Central America and Mexico to the coast of California to
southern British Columbia (Calambokidis et al, 2000, Calambokidis et al 2001). Their
migration routes in summer and fall months range from Central America and Mexico to
coast of California to southern British Columbia (Calambokidis et al, 2000,
Calambokidis et al 2001). Peak humpback abundances along the west coast of the North
America are estimated to be between summer and fall, depending on geographic region.
The literature suggests that there is an increased chance of humpbacks encountering
vessels during these months of peak abundances. Numerous studies find a positive
relationship between seasonal abundance and risk of collisions on humpback whales. For
example, in an analysis of historical humpback whale and vessel collision data from
33

Hawai’i during 1975-2011, 75% of reported collisions happened between February and
March, peak whale season for Hawai’i, suggesting a relationship between whale density
and frequency of collisions (Lammers et al., 2013). In addition, Currie et al. (2015) found
that risk of vessels encountering humpbacks varied month to month, with an increase
during Hawai’i’s peak whale season. Similarly, Neilson et al. (2012) found seasonal
trends in their summary of 108 cetacean-vessel collisions in Alaska from 1978-2011,
with 91% of the humpback related collisions occurring May-September. Together, these
various studies demonstrate that humpback whale abundances vary seasonally and
suggest that high abundances of collisions can be predicted during peak whale season.

2.5.1.3 Sea State and Age Class
Sea state refers to wave condition at sea and is typically classified using the
Beaufort scale. This is an important measure that is considered by boatmen/sailors when
maneuvering ships at sea. The Beaufort scale ranges from 0, indicating the sea surface is
smooth and mirror-like, to 12 indicating hurricane like conditions, with waves over 45
feet (NOAA, n.d.). Research documents that an increase in Beaufort scale decreases
one’s ability to detect cetaceans (Demaster et al., 2001; Teilmann, 2003; Dolman et al.,
2006; Barlow & Taylor, 2005; Williams et al. 2016). One study demonstrated a 15-20%
reduction in ability to detect a humpback 300m away when sea state increased from 0 to
4 on the Beaufort scale (Currie et al., 2015). Understanding the effect of sea state on boat
operators’ or on-board observers’ ability to detect whales is crucial to invoke measures of
avoidance (Williams et al., 2016).
Cetacean age classes are classified as 1) calves who rely on their mother for milk,
2) sexually immature juveniles, and 3) sexually mature adults. In the literature, there is
34

evidence suggesting that the age class of a cetacean can determine an individual’s
susceptibility to a ship strike (Carrillo & Ritter, 2010; Currie et al., 2015; Lammers et al.,
2013). Scientists hypothesize that calves and juveniles are more susceptible due to traits
such as spending more time at surface to breath compared to adults, being less visible
than adults, and being naïve to interactions with vessels (Laist et al., 2001; Paniganda et
al., 2006). For example, Carrillo & Ritter (2010) found that 44% of cetacean carcasses
documented (59 total) from vessel collisions between 1991-2001 in the Canary Islands
were either calves or juveniles, compared with only 25% being adults. In another study
conducted on Hawai’ian humpback whales, calves and juveniles were found to be more
vulnerable to cetacean-vessel collisions (Currie et al., 2015). In an analysis of historical
Hawai’ian cetacean-vessel collision records from 1975-2011, ~64% of 52 collisions
involved either a calf or juvenile (Lammers et al., 2013). Neilson et al. (2012) found in
their summarized report of 108 cetacean-vessel collisions on seven different whale
species in Alaskan waters from 1978-2011 that calves and juveniles appeared to be at
higher collision risks than adults. Knowlton & Kraus (2001) found that calves and
juveniles accounted for 53% of documented severe injuries from fishery interactions and
vessel collisions on Atlantic right whales.

2.5.1.4 Vessel Speed
The speed at which a vessel is traveling can influence the whale’s ability to
perform evasive maneuvers to avoid a ship strike. Numerous studies have been conducted
to analyze the relationship between vessel speed and probability of a fatal cetacean-vessel
collision. Silber et al. (2010) found a direct relationship between vessel speed and
severity of injury in relation to cetacean-vessel collisions. Transect surveys, carried out
35

monthly during the winter in Hawai’i from 2013-2015, suggest that vessels moving at
speeds over 12-13 knots (kts) increase the likelihood of cetacean-vessel collisions (Currie
et al., 2015). Wiley et al. (2011) modeled lethal risk reductions of cetaceans; specifically,
humpback, fin, and wright whales, with overall findings suggesting that restricting speeds
to 10 kts reduced probability of lethality by 56.7%. Lammers et al. (2013) suggests that
vessel speeds above 12 kts decrease whale’s ability to avoid vessels and collisions above
this speed increase probability of a fatal cetacean-vessel collision. Based on models of
humpback and fin whale sighting data and environmental covariates, the western portion
of the Strait of Juan de Fuca is a high-risk area of fatal cetacean-vessel collisions because
the Strait has higher than average vessel speeds (>12 knots) and high-density marine
traffic (Figure 2.6; Nichol et al., 2017). Understanding relationship of vessel speed and
probability of fatal collisions is important when analyzing current mitigation practices for
reducing cetacean-vessel collisions.

36

Figure 2.6: Map showing highest risk areas of a lethal collision for humpback (top)
and fin whales (bottom) along coast of Vancouver Island, BC and at the mouth of
the Strait of Juan de Fuca from GAM model estimates. Whale sighting data obtained
from aerial surveys from 2012 – 2015 and shipping data was obtained from 2013
AIS ship traffic data. (Image adapted from: Nichol, L.M., Wright, B.M., Hara, P.O.,
& Ford, J.K. (2017). Risk of lethal vessel strikes to humpback and fin whales off the
west coast of Vancouver Island, Canada. Endangered Species Research, 32, 373390.)

2.5.1.5 Ship Strike Conclusion
The major findings have determined that there are multiple factors that come into
play when trying to determine the probability of a fatal cetacean-vessel collision:
seasonal variation, sea state, age class, and vessel speed. While certain factors have a

37

greater effect than others on the probability of a fatal collision, it is important to take each
into consideration. A high sea state paired with high vessel speed is suggested to have a
greater negative effect on detecting a cetacean in time for avoidance measures like
altering route and slowing down (Wiley et al., 2011; Teilmann, 2003). The susceptibility
of calves and juveniles in relation to seasonal variation should be considered because
calves and juveniles are likely to be present during peak migration periods, increasing the
probability of cetacean-vessel collisions (Neilson et al., 2012). In April of 2017, a gray
whale from the North Puget Sound Feeding Group was struck, and survived, by a
recreational vessel off the coast of Whidbey Island (Lewis, 2017). A whale watching boat
struck a humpback whale, who survived, near Race Rocks Ecological Reserve, Canada
while traveling between 24-28 knots (Lawrence, 2017). These incidences, though not
fatal, further strengthen how necessary it is to evaluate current mitigation practices that
are in place to reduce cetacean-vessel along the coast of Washington and
Northern/Central Oregon. In the most recent marine mammal stock assessment for
humpbacks along the west coast, over a five-year period (2012-2016) it is estimated that
2.1 whales on average are taken each year due to ship strikes (Carretta et al., 2019).

2.5.2 Entanglement
Entanglement has dire and lasting consequences to whales, with the main cause of
death from entanglement being drowning due to asphyxiation (Moore & Hoop, 2012;
Dolman & Moore, 2017). Entanglement has the potential to prevent the whale from rising
to the surface to breath, resulting in death. A study by Cassoff et al. (2011) suggests that
age class may predispose a whale to drowning, with younger age classes being more at
risk. If the whale manages to survive initial entanglement, they suffer from a variety of
38

internal and external injuries. Gear that is entangled around or near the mouth can disrupt
feeding, thus leading to starvation (Kot et al., 2009; Cassoff et al., 2011; Moore & Hoop,
2012; Dolman & Moore, 2017). Lacerations leave the body open to bacteria that can lead
to infection which then leads to a weakened immune system and opens them up for more
infections, and eventually death (Cassoff et al., 2011; Moore & Hoop, 2012).
Entanglement can trigger behavioral and physiological stress responses. Exposure to
prolonged chronic stress can weaken their immune system, making them prone to fatal
infections and diseases (St. Aubin & Dierauf, 2001; Cassoff et al., 2011). Exposure to
prolonged entanglement also causes severe tissue damage, leading to continual chronic
pain until subsequent death (Cassoff et al., 2011).
Alaska, California, Idaho, Oregon, and Washington are part of an interstate
compact agency, The Pacific States Marine Fisheries Commission (PSMFC) that assists
in resource management of various agencies and the fishing industry to prevent
unsustainable use of Pacific Ocean resources. Each state is represented by three
commissioners and the PSMFC is required to meet at least once a year. Yearly meetings
provide an opportunity for each state to identify priority issues and vote for resolutions.
PSMFC received a NOAA Bycatch Reduction Engineering Program grant to use towards
testing the most promising innovations that could reduce entanglements of marine
mammals, with particular focus on humpbacks in crab pot gear.

2.5.2.1 Current Trends
In 2017, NOAA Fisheries reported that there were 76 confirmed entanglement
cases of large whales. Humpback whales were the number one whale species entangled
(n=49) in 2017 (NOAA Fisheries, 2017). Since 2007, humpbacks represent 68% of large
39

whales reported entangled (NOAA Fisheries, 2017). The confirmed cases of
entanglement reports occurred along all U.S. coasts except the Gulf of Mexico and more
than half of all reports occurred off waters of two states: California (32.9%) and
Massachusetts (26.6%) (NOAA Fisheries, 2017). A high number of confirmed
entanglements for humpbacks occurred off the coast of the main Hawai’ian Islands
(14.3%) (NOAA Fisheries, 2017). It is estimated that 70% of the entanglements were
from fishing gear and 24% in line of unknown origin. Along the US West Coast
specifically, NOAA Fisheries – West Coast Region, report 31 whales confirmed
entangled off the coast of Washington, Oregon, and California. Of the 31 confirmed
reports, humpbacks made up roughly half (n=16) (NOAA Fisheries, 2018). The number
once source of entanglement along coasts of Washington, Oregon, and California was
entanglement in commercial and recreational Dungeness crab traps (NOAA Fisheries,
2018). More than half the confirmed reports were off central and southern California
(80%) and the rest occurring off Oregon and Washington (19%) (NOAA Fisheries,
2018). In the most recent marine mammal stock assessment for humpbacks along the
west coast, over a five-year period (2012-2016) it is estimated that 15.7 whales on
average are taken each year due to entanglement in fishing gear (Carretta et al., 2019).

2.5.3 Other Threats
Along with facing threats from ship strikes and entanglement in fishing gear, they
also face negative impacts from other threats with anthropogenic origins like ingestion of
microplastics and noise pollution. Microplastics are generally defined as plastic particles
smaller than 5mm and are a unique threat because of their ability to absorb and
concentrate various toxic pollutants (Fendall and Sewell, 2009; Betts, 2008; Moore,
40

2008, Andrady, 2011). The Great Pacific Garbage Patch, located in the NP, is estimated
to be twice the size of Texas and contain an estimated 7 million tons of trash (Craens,
2012). Eighty percent of trash in the garbage patch is estimated to be plastic, contributing
to the threat of microplastics (Craens, 2012). Microplastics are associated with persistent,
bioacculmulative, and toxic organic contaminants (PCBs, PAHs, PBDEs) which are
known to have adverse effects on organisms such as reduced growth rate, decreased
reproductive output, and reduced offspring viability, which can all result in population
declines (Galloway & Lewis, 2016). Deaths at sea and natural decay of organisms before
necropsies can be performed decreases opportunities to document microplastics in baleen
whales (Besseling et al., 2015). Due to difficulties of directly observing plastic ingestion
by whales, many scientists are looking at their prey source, and determining the potential
a whale species has of ingesting plastics based on prey source (Au et al., 2017; Egbeocha
et al., 2018; Burkhardt-Holm & N'Guyen, 2019). It has been theorized that baleen whales
were ingesting plastics, but it was not confirmed until 2012 (Besseling et al., 2015). After
necropsying a juvenile female humpback whale, Besseling et al (2015) found 45 plastic
particles, ranging in size from 0.04 mm – 5.8 mm. They only examined a fifth to a tenth
of the total length of the humpback’s intestine, and therefore estimate that the total
amount of plastic consumed could be five to ten times higher than what they found. Due
to potential to cause population decline if there is enough plastic accumulation within
populations, management efforts should also focus on ways to reduce the number of
plastics entering the oceans.
Whales are dependent on sound for various behaviors such as communication,
navigation, and foraging and the impact anthropogenic noise can have on these vital

41

behaviors has been a topic of increasing concern as human activity increases in the
oceans. Sources of ocean noise include military sonar, commercial shipping, marine
geophysical surveys, marine construction, whale watching, and aircraft, with increased
ocean noise suggested to have negative behavioral, acoustic, and physiological responses
(Todd et al., 1996; Croll et al., 2001; Williams et al., 2006; Nowacek et al., 2007; Miller
et al., 2009; Rolland et al., 2012; Sivle et al., 2015; Isojunno et al., 2016; Dunlop, 2016).
Due to the difficulty to track the number of deaths a year by these other anthropogenic
threats, there is no estimate available on how many deaths a year are caused by noise
pollution or plastic ingestion. This, in turn, effects the total number of whales killed each
year by anthropogenic activities, thus reducing accuracy of how many whales are killed
each year due to humans.

42

: Methods
Cascadia Research Collective and Washington Department of Fish and Wildlife
(WDFW) jointly designed and implemented vessel-based line-transect surveys for the
west coast of Washington State and Northern/Central Oregon from 2011 – 2013. The
project was part of a WDFW Section 6 Grant with overall objectives to conduct research
on Endangered Species Act (ESA)-listed marine mammals. The project aimed to
determine abundance, distribution and habitat use, gather information on stock structure,
and identify areas of human interaction including ship strikes, entanglements, and other
fishery interactions for the California/Oregon/Washington stock of humpback whales
along the US West Coast. To achieve the Section 6 Grant objective, surveys were
planned to sample the study area every spring, summer and fall from 2011 to 2013.
Transect design and placement were determined to specifically include deep and shallow
waters, with focus on overlapping shipping lanes and surveying over the continental shelf
and various underwater canyons (Error! Reference source not found.). Access to o
vernight harbors and distance from Greys Harbor, WA, in conjunction with weather
windows dictated how far south the survey area could extend.
This thesis compiles and analyses data from these line-transect surveys to address
the following species-specific objectives for humpback whales:
1. Examine the distribution of sightings of humpback whales along
the outer coast of Washington and Northern/Central Oregon
2. Determine density and abundance estimates
3. Examine if these estimates of abundance display any seasonal
trends or differences with estimates obtained in other surveys

43

3.1 Survey Area
Survey lines were numbered from north to south beginning at Neah Bay,
Washington and ended at Newport, Oregon with western boundaries of the survey area
varying from 31 to 43 nmi from shore, encompassing a total area of 9967 nmi2 (Figure
3.1). Within this survey region is the Olympic Coast National Marine Sanctuary
(OCNMS), one of North America’s most productive marine regions and a vital area for
numerous ecologically and commercially important species, including humpback whales
(Basta, 2011). Transect lines were specifically created to cover the continental shelf and
major submarine canyons. It has been suggested that whales congregate to submarine
canyons and continental shelves, seasonally and year-round, due to oceanographic
mechanisms that occur within these underwater features (Moors-Murphy, 2014). The
transect lines traversed major shipping lanes accessing the Puget Sound on the northern
end of the study area and the Columbia River at the border of Washington and Oregon.
The northernmost part of the study area encompasses the entire OCNMS, 2408 nmi2. It
includes most of the continental shelf and parts of three major submarine canyons, Nitinat
Canyon, the Quinault Canyon, and the Juan de Fuca Canyon (Figure 3.1). The southern
portion of Oregon extends to include another major submarine canyon, Stonewall Bank.

44

Figure 3.1: Designated survey area outlined in black for systematic line-transect
surveys by Cascadia Research Collective and WDFW on vessel G.H. Corliss from
2011 – 2012. Regions are identified as [A]: Washington; [B]: Northern/Central
Oregon. Dashed box outlines the Olympic Coast National Marine Sanctuary. Red
lines are the navigated transect lines and their corresponding transect number.
Major submarine canyons are identified. Major cities labeled. Light grey lines
represent bathymetric contours.

45

3.2 Data Collection
The same survey protocol was followed for the 18 established transects, starting
from Neah Bay, WA and ending at Newport, Oregon (Figure 3.1). Each survey consisted
of four personnel: 2 observers, 1 data recorder, and the captain. Environmental conditions
(Beaufort, visibility, swell height) were recorded every half hour or more often if
conditions changed during surveys. The program WinCruz was used during the first half
of the project to record effort, environmental conditions, and sighting data, then switched
to Access database. To be considered on-effort, two observers needed to be actively
searching with a combination of naked eye and 7x50 fujinon binoculars, the vessel had to
be traveling on the established survey lines at > 5 knots and weather conditions had to be
acceptable. Acceptable weather conditions were considered Beaufort 5 or less, visibility
greater than 0.5 nautical miles, and no rain. While on-effort, observers continually
scanned from the boat to horizon looking for marine mammals. If the vessel slowed
below 5 knots or if observers experienced drastic weather change such as dense fog, the
survey would go off-effort. To address observer fatigue, observers would rotate with data
recorder, spending one hour observing on portside, one hour observing on star board side,
and then one hour as data recorder. On-effort observations were made until they ran out
of daylight, conditions worsened, or made it into the harbor.
When a marine mammal sighting was made, the time, latitude/longitude, ship
heading, angle from angle board, and reticle from binoculars or estimated distance in
meters was recorded. Marine mammals were identified to the species level if possible,
and cluster size was estimated. If the species could not be identified, all observations
were recorded in the comments section that could aid in species identification later.

46

3.3 Data Analysis
The survey area was digitized using ArcGIS Pro 2.2.3. The survey GPS points
used for navigating from transect to transect were connected to create a polygon feature.
The eastern boundary of the survey area was traced and snapped to the west coast
shoreline layer obtained from the ESRI Online data portal. Because of inconsistent
survey coverage, data was post-stratified by region, either Washington (WA) or
Northern/Central Oregon (OR). The data were analyzed using the software Distance
following methods outlined in Buckland et al. (2001) for ‘conventional distance
sampling’ (CDS) and ‘multiple covariate distance sampling’ (MCDS; Distance version
7.2; Thomas et al., 2010). The analysis was divided into three parts: 1) fitting the
probability density function f(x), 2) estimating mean cluster size based on observed
cluster sizes, and 3) estimating abundance and density in Distance with the following
formulas as mentioned in section 3.2.2 of the Literature Review:
Density:

̂=
𝐷

̂
Abundance: 𝑁

=

𝑛𝑓̂ (0)
2𝐿
𝑛𝑓̂ (0)𝐴
2𝐿

A global f(x) (the probability density function) was fitted for large whales that
included sightings from fin and humpback whales. Initially, if a cetacean could not be
identified to the species genus level it was identified to the group level (i.e., large whale,
small whale, etc.). To increase sample size, unidentified large whale counts were prorated
into fin whale or humpback whale counts. This was only used in distance analyses and
not in reporting of raw sighting data nor mapping of humpback whale distributions. They

47

were included in estimating large whale’s detection function and included in speciesspecific abundance and density estimates.
Due to small sample size, stratified abundance and density was only estimated for
humpback whales once an appropriate detection function was selected. Humpback whale
sightings were pooled across regions, seasons, and years to generate an overall abundance
and density estimate for the study area from April – December. Estimates were generated
for four levels of stratification: 1) pooled, 2) regional, 3) region and year, and 4) region
and season.

3.3.1 Estimating Probability Density Function and Cluster Size
The probability density function (pdf), f(x), describes the relationship between
distance and probability of detection (Buckland et al., 2001). CDS and MCDS was used
in Distance to estimate best fit of f(x) following methods described in Buckland et al.
(2001). Hazard-rate and half-normal key functions with no series expansions were fit to
the distribution of observed distances. These functions often provide a good fit when
modeling f(x) (Buckland et al., 2001, 2004; Thomas et al., 2010).
Visual exploration of the data suggested that the data should be truncated to 1.5
nmi to improve ability to fit f(x) (Error! Reference source not found.). For MCDS, e
xploratory analyses indicated that Beaufort and visibility had the most substantial effect
on detecting a whale. Beaufort was used as a continuous variable (on a scale of 0-6) and
as a four-factor variable (0, low, medium, high) (Error! Reference source not found.), w
hile visibility was used just as a continuous variable. When looking at the distribution of
recorded Beaufort as a continuous variable, it is unusual for there to be a spike in

48

sightings after Beaufort 3. Visibility and Beaufort were analyzed using 1-way ANOVA in
R to determine if seasonal averages varied significantly.
In the final modeling of pdf(0), hazard-rate and half-normal key functions with no
series expansions were fit to the distribution of observed sighting distances. The
covariates of Beaufort continuous, categorized Beaufort, and visibility continuous were
used for MCDS models. Model of best fit was chosen based on AIC values and
examining detection plots. This provided the f(0) value necessary for Distance to estimate
abundance and density, and thus provided final regional and seasonal abundance and
density.

49

Figure 3.2: Histogram of perpendicular distances of large whales sighted during G.H.
Corliss line-transect surveys by Cascadia Research Collective and WDFW from 2011 –
2012 along coast of Washington and Northern/Central Oregon.

Average perpendicular distance of sightings (nmi)

0.6
0.5
0.4
0.3
0.2
0.1
0
0

1

2

3

4

5

0.6
0.5
0.4
0.3
0.2
0.1
0
0

Low

Medium

High

Beaufort

Figure 3.3: Perpendicular distances of large whale sightings by beaufort
categorized as (top) continuous variable, and (bottom) as a four-factor
variable as used in MCDS analyses.

50

Results
4.1 Survey Effort and Model of Best Fit
4.1.1 Survey Effort
Regional effort varied by season and year, with Washington having the most
consistent survey coverage by season and year (Table 4.1). Between 2011 and 2012 five
systematic line-transect surveys, broken up into 12 cruises covering over 2,044 nmi of
transects and an area of 9,967 nmi2, were conducted from the WDFW patrol vessel G. H.
Corliss. Surveys done in spring 2011, summer 2011, and spring 2012 had complete
survey coverage whereas surveys done in fall 2011 and summer 2012 failed to fully
survey the entire study area. For fall 2011 and summer 2012, weather, personnel, and
boat availability prevented the survey being conducted down into Oregon. The 2013
survey data was omitted because of inconsistent survey coverage.

51

Table 4.1: Summary of on-effort surveys conducted from
G.H. Corliss line-transect surveys by Cascadia Research
Collective and WDFW from 2011 – 2012 along coast of
Washington (WA) and Northern/Central Oregon (OR).

Spring

Effort
(nmi)
282.4

# of
transects
9

2011

Summer

263.3

10

2011

Fall

223.7

8

2012

Spring

298.1

10

2012

Summer

199.9

7

2011

Spring

222.4

7

2011

Summer

296.3

9

2011

Fall

-

-

2012

Spring

257.9

7

2012

Summer

-

-

Total

2,044.1

Region

Year

Season

WA

2011

OR

Area
(nmi2)
5,204

4,763

9,967

Table 4.2: Summary of total effort (nmi), number of transects
covered, and average of environmental variables measured for each
survey from G.H. Corliss line-transect surveys by Cascadia Research
Collective and WDFW from 2011 – 2012 along coast of Washington
and Northern/Central Oregon.

52

Survey
Dates

Season/Year

#
transects

Effort
nmi

Beaufort

Visibility
nmi

30Apr-01May.
04-06May

Spring 2011

16

504.8

2.95

8.2

06-07May.
29-31May

Spring 2012

17

556.1

1.50

8.1

23-24Aug.
14-16Sept,

Summer 2011

18

559.6

1.28

8.5

19Jul.
16-18Aug

Summer 2012

7

199.9

0.54

4.0

06-08Dec

Fall 2011

8

223.7

2.33

7.5

Environmental variables have an impact on an observer’s ability to detect an
animal at sea. As Beaufort increases or visibility decreases, the ability to detect a whale
decreases. These variables are often used as covariates in multiple-covariate distance
sampling (MCDS) analyses to generate abundance and density estimates. Beaufort and
visibility were continually recorded throughout all surveys. Visual exploration of the data
suggested that the data should be truncated to 1.5 nmi to improve ability to fit f(x), as
noted in the previous section (Figure 3.2). For MCDS, exploratory analyses indicated that
Beaufort and visibility had the most substantial effect on detecting a whale. Beaufort was
used as a continuous variable (on a scale of 0-6) and as a four-factor variable (0, low,
medium, high), while visibility was used just as a continuous variable. When looking at
the distribution of recorded Beaufort measurements as a continuous variable, it is unusual
for there to be a spike in sightings after Beaufort 3 (Figure 3.3). A one-way ANOVA
was conducted on seasonal Beauforts to determine if season had significant impact on
average beaufort, and thus an impact on observer’s ability to detect a whale. There was
significant difference in average Beaufort by season at the p<0.05 level (1-way ANOVA,
F2=579.12, P<0.0001). Post hoc comparisons using the Tukey HSD test indicated
seasonal averages varied significantly across all seasons (p<0.01), with spring 2011
having the highest average beaufort of 2.9 and summer 2012 having the lowest average
beaufort of 0.53 (Table 4.2). Examining humpback encounter rate by beaufort, we see an
increase in encounter rate at beaufort 5, but with less effort (Table 4.3).

53

Table 4.3 Number of humpback whale sightings, effort, encounter
rate, mean cluster size, and average perpendicular distance by sea
state from G.H. Corliss line-transect surveys by Cascadia Research
Collective and WDFW from 2011 – 2012 along coast of
Washington and Northern/Central Oregon.

Beaufort
0
1
2
3
4
5
6

54

L nmi
230.8
746.7
530.4
293.4
213.6
28.9
0.2

n
17
32
15
1
6
11
0

n/L per
100nmi
7.4
4.3
2.8
0.3
2.8
38.1
-

mean
cluster size
1.5
1.5
1.4
1.0
1.8
1.3
-

Avg. Perp
Distance nmi
0.3
0.2
0.7
0.0
0.3
0.6
-

4.1.2 Model of Best Fit
Distance 7.3 was used to determine model of best fit of the detection function
over distribution of perpendicular sightings for large whales. The key component in
obtaining abundance and density in distance sampling is the estimation of a detection
function. The detection function describes the relationship between distance and
probability of detection (Buckland et al., 2001). The model of best fit based on lowest
ΔAIC was the hazard-rate with visibility as covariate and thus used to determine
estimates for the four different levels of stratification of humpback whales (Table 4.4;
Figure 4.1). The estimated effective strip width (ESW) for large whales is 0.43 nmi.

Table 4.4: Summary of model selection statistics and parameter estimates for
models proposed to fit perpendicular distance data for large whale sightings from
G.H. Corliss line-transect surveys by Cascadia Research Collective and WDFW
from 2011–2012 along the coast of Washington and Northern/Central Oregon.
Model of best fit ( hr + vis ) chosen by lowest AIC value (ΔAIC = 0).
Model + covariates
hr + vis
hr
hr + bft cat
hr + bft cat + vis
hr + bft
hr + vis + bft
hn

ΔAIC
0
2.26
5.24
6.43
8.69
10.1
12.1

# par
3
2
5
6
3
4
1

Model Parameters
ESW CV
pdf (0)
0.43
0.44
0.54
0.53
0.57
0.56
0.61

0.09
0.15
0.09
0.09
0.08
0.09
0.06

2.33
2.29
1.85
1.88
1.75
1.79
1.64

P

CV

GOF K-S p

0.29
0.29
0.36
0.36
0.38
0.37
0.41

0.09
0.15
0.09
0.09
0.08
0.09
0.06

0.641
0.641
0.204
0.192
0.235
0.334
0.026

hr hazard rate, hn half normal, vis visibility (nmi), bft cat beaufort category (0, low, med, high), bft beaufort
numerical, ΔAIC delta Akaike Information Criterion, # par number of parameters, ESW effective strip width, CV
coeffecient of variation, pdf (0) probability density function at 0, GOF K-S p Goodness-of-fit Kolmogorov
Smirnov test probability.

55

Figure 4.1: Fitted hazard rate with visibility as covariate MCDS model (red curve)
to distribution of large whale perpendicular distances (histogram).

56

4.2 Humpback Abundance and Density Estimates
4.2.1 Raw Data
Within the entire study area, humpback whales were the most seen of the large
whale group, consisting of 68 sightings of 100 individuals during all surveys without
truncating the sightings by distance from boat. After prorating sightings of unknown
large whales, there are an estimated 82 humpback whale sightings consisting of 119
individuals (Table 4.5). These prorated estimates are used in Distance to estimate
abundance and density. Group size ranged from 1 to 7 (mean = 1, SD = 2). The largest
number of sightings was made during the summer 2011 survey, with 37 sightings of 57
individuals.

Table 4.5: Summary of raw large whale sightings made during on-effort
before truncation for surveys conducted from G.H. Corliss line-transect
surveys by Cascadia Research Collective and WDFW from 2011 – 2012
along coast of Washington and Northern/Central Oregon. Before includes
unidentified large whales and after shows
N sightings (total indvs.)
Species

Before

After

Fin whale, Balaenoptera physalus

5 (10)

9 (14)

68 (100)

82 (119)

Humpback whale, Megaptera novaeangliae
Unidentified large whale
TOTAL

18 (23)
91 (133)

Sightings were concentrated around various submarine canyons throughout the
entire study area. An area termed “the Prairie”, the area between Juan de Fuca Canyon
and outer edge of the continental shelf identified by Calambokidis et al. (2004), had
consistent sightings each survey and highest concentration of sightings overall (Figure
4.2). Farther south, there was a high concentration of sightings around Stonewall Bank
57

during summer 2011. Summer 2011 had the most sightings, concentrated around the
Prairie, Juan de Fuca Canyon to the north and Stonewall Bank to the south, with few
sightings in between. Fall 2011 appears to have the most even distribution of sightings
compared to other seasons and years. Spring 2012 had the least number of sightings, and
uneven distribution of sightings, all of them occurring to the north near the Prairie and
Juan de Fuca Canyon or further south near Willapa Canyon and Seachannel. There were
no sightings of humpbacks in spring 2011 despite surveys being conducted in good
weather conditions and covering almost all 18 transects.

58

Figure 4.2: Distribution of raw sightings of humpback
whales made during line-transect surveys from 2011 –
2012 and important underwater canyons identified.
Geographic strata include: (A) Washington and (B)
Northern/Central Washington. Dashed outline
represents boundary for Olympic Coast National
Marine Sanctuary.

59

4.2.2 Abundance and Density Estimates
For the entire study area from 2011-2012, it is estimated 82 humpback whales
were sighted over 2,044 nmi of effort. Modeled results estimate the abundance for the
survey area to be 2,205 (CV=0.26) humpbacks with an estimated density of 6 (CV=0.26)
humpbacks per 100 nmi2 (Table 4.5). Seasonal effort was variable, with a range of 2241,061 nmi surveyed. Summer had the most humpbacks sighted, with an estimated density
of 4 (CV=0.23) humpbacks per 100 nmi2 and an estimated abundance of 1406
(CV=0.32) throughout the entire area. Although abundance and density estimates appear
to differ by season, the 95% CI for abundance and density estimates overlap; therefore,
there are no significant difference in seasonal estimates for abundance and density (Table
4.5).
Regional effort for Oregon was inconsistent throughout the study period with no
effort conducted in the fall 2011 nor summer 2012. Humpbacks were only sighted in
2011, despite having 258 nmi of effort in the spring of 2012. Even though there was
comparable effort between spring 2011, summer 2011, and spring 2012 (ranging from
222 – 296nmi of survey effort), only summer 2011 had sightings, with a modeled
abundance estimate of 689 (CV=0.51) whales and a density of 2 (CV=0.51) humpbacks
per 100 nmi2 throughout the study period (2011-2012) (Table 4.5).
Regional effort for Washington was comparable across all surveys ranging from
200 – 298nmi of effort, with a combined total of 1267.431 nmi surveyed. Washington
had a modeled abundance estimate of 1516 (CV=0.3) whales and a density of 4
(CV=0.29) humpbacks per 100 nmi2 throughout the study period (2011-2012) (Table
4.5). Modeled seasonal abundance estimates range from 276-524 (CV=0.65, 0.59)

60

humpbacks, with an average of 303 humpbacks per season. Spring 2011 had the lowest
estimate of 0 whales sighted, followed by spring 2012, which had an estimated
abundance of 276 (CV=0.65) humpbacks. Fall 2011 had the highest modeled estimate of
524 (CV=0.59) humpbacks. Modeled density estimates range from 0.79-1.49 (CV=0.65,
0.59) humpbacks per 100 nmi2.

61

62
Total

OR

WA

OR

WA

WA&OR

82

67

2044.1

4.0

6.3(0.26)

not surveyed

296.3
9

Fall

2.0(0.51)

8.4

480.4

14

0

0.8-5.1

2205(0.3)

689(0.5)

265-1794

176-1556
524(0.6)
0.5-4.4
1.5(0.59)
8.5
223.7

8

19

Fall
25

85-898
334-1537
276(0.6)
717(0.4)
0.2-2.6
1.0-4.3
0.8(0.64)
2.0(0.40)
1.7
5.6
580.5
463.2

19
17

10
26

Spring
Summer
Spring

85-898
757-2611
176-1556
276(0.6)
1406(0.3)
524(0.6)
0.2-2.6
2.2-7.4
0.1-4.4
0.9(.65)
4.0(0.32)
1.5(0.59)
0.9
6.7
8.5

1061
760
224

33
26
8

10
51
19

Spring
Summer
Fall

Summer

280-1314
265-1794
606(0.4)
689(0.5)
0.8-3.7
0.8-5.1
1.7(0.40)
2.0(0.51)
-

4.6
4.8
-

498
519
258

405-2042
910(0.4)
1.2-5.8

2.6(0.42)

17
16
7

769

27

34

2011

265-1794
689(0.5)
0.8-5.1

2.0(0.51)

23
25
0

4.4

777

23

25

OR

95%CI
848-2710
N(CV)
1516(0.3)
95%CI
2.4-7.8

2012
2011
2012

3.2

Lnmi
1267

n
57

WA

Model Estimates
n/L per
D/100nmi2(CV)
100 nmi
4.3(0.29)
4.5

#
transects
44

Survey Effort

n number of sightings, L total length of transect lines nmi, n/L encounter rate, D density, N abundance, CV coefficient of variation

Pooled

Region,
Season

Season

Region,
Year

Region

Stratification

Table 4.5: Number of sightings with associated total survey effort and summary of modeled results using
hazard-rade MCDS model with visibility as covariate and truncation distance of 1.5 nmi. Results separated by
the different levels of stratification used when modeling results for humpback whales along coast of
Washington and Oregon from 2011-2012.

: Discussion
5.1 Humpback Whale Abundance & Density
A number of researchers have generated abundance and density estimates for the
California/Oregon/Washington stock of humpback whales along the US West Coast
(Barlow, 1994; Calambokidis et al., 1999, Calambokidis et al., 2003; Barlow et al., 2011;
Barlow, 2016; Wade et al, 2016; Calambokidis et al., 2017). This is the first study to
perform surveys specifically to examine sighting data to estimate multiple seasonal trends
(see Barlow, 2016; Calambokidis et al., 2004). This discussion will focus mainly on
results generated for Washington because this region had more data and thus able to draw
more conclusions. In this study, it is estimated that there are a total of 2,205 humpback
whales migrating through the area between 2011-2012. If we compare that to the annual
marine mammal stock assessment reports generated by NOAA Fisheries in 2018, which
used data collected during a similar time period (2011-2014) as this study (2011-2012),
they estimated abundance of the California/Oregon/Washington stock to be 2,900
(CV=0.048) animals. This is comparable to the estimates generated here of 2,205
(CV=0.26) animals.
Overall, the distribution of whales was not uniform throughout the study area,
with sightings tending to aggregate at specific submarine canyons (the Prairie, Juan de
Fuca Canyon, Stonewall Bank, etc., Figure 4.2). This is not surprising because it is well
documented in the literature that deep sea canyons can serve as important feeding
habitats for whales (Benson et al., 2002; Calambokidis et al., 2004; Rosa et al., 2012;
Moors-Murphy, 2014).

63

Whales are seasonal migrators, so it is expected to see them occupying different
habitats throughout the year as they migrate from their southern breeding grounds in
warmer waters to their northern feeding grounds in colder, productive waters. Where they
are spending time in the interim is a topic of importance for management and
conservation purposes. Seasonal estimates aid in identification of abundance and density
fluctuations within a particular area of interest, highlighting instances of variations. There
were statistically significant differences in humpback whale abundances between
seasons, with the highest estimate in summer and the lowest in spring. Having dedicated
seasonal survey effort, it can further be determined a range of specific dates we can
expect humpbacks to be in an area. It is established in the literature that humpbacks are in
this area starting in May. Spring 2011 had no sightings of humpback whales despite
being done in early May, having good survey coverage, and overall good weather
conditions. If we compare the spring 2011 survey to the spring 2012 survey, we see that
spring 2012 was conducted later in May, suggesting that the spring 2011 survey was
conducted too soon in the season thus potentially explaining why there were no
humpbacks sighted during spring 2011 survey. This suggests that whales are not in this
area until later in May. The latest surveys were conducted was December 6-8, with an
estimated abundance of 524 humpbacks. This suggests that humpbacks are still present in
this area until at least the middle of December.
Marine mammal abundance surveys are vital for determining high density areas
and detecting changes in populations. Seasonal estimates provide more detailed look at
when and how long these animals are spending in a particular area during a given time of
the year. For example, off the Coast of Virginia, it has been discovered that North

64

Atlantic Right Whales (Eubalaena glacialis) occupy this area between November 1st –
April 30th (Mallette et al., 2018). This led to the implementation of a seasonal
management area, where vessels that are larger than 65 ft are required to reduce their
vessel speed to less than 10 kts while traveling through the area to reduce likelihood of
injury to these animals (Mallette et al., 2018).
Along the Pacific Coast of the United States, fatal collisions with vessels and
entanglement with trap and pot line fishing gear is the most common source of injury
(Carretta et al., 2016). Current entanglement reduction efforts are focusing mainly on
fishing line modifications that ether reduce the amount of time a line is in the water
column (galvanic time released devices, acoustic buoy releases) or altering composition
of the line so that it breaks easier when a whale does become entangled (changing line
color, changing line material/strength, adding weak links, timed tension-line cutters)
(Lebon & Kelly, 2019). The biggest hurdle here is identifying ways to reduce
entanglement while avoiding unnecessary repercussions to the fishing community.
Continuing research efforts should focus on identifying effective methods of reducing
entanglements that will not negatively impact fishermen’s livelihoods from financial
constraints. To reduce the risk fatal vessel collisions, studies suggest reducing vessel
speed or location restrictions to be the most effective methods to implement is areas of
high overlap between vessels and whales (Calambokidis et al., 2019; Lammers et al.,
2013; Wiley et al., 2011). No major action has been taken to implement any of the
suggested measures recommended to reduce fatal vessel collisions on the West Coast of
the US. Efforts should still be made to understand an animal’s seasonal use of a particular
geographic region to encourage and aid in development of the most effective

65

conservation management strategies as well as providing valuable information on
movement patterns, habitat use, and population demographics.
Inconsistencies in the survey coverage led to small sample size with large
coefficient of variations, thus results should be interpreted as conservative and limit the
amount of regional and seasonal comparisons that can be made. The results do provide
general insight in the probable seasonal and yearly abundance and density trends.
Surveys only happened in Oregon for three seasons, and only one season had any
sightings, thus severely limiting any conclusions that can be made about yearly or
seasonal abundance and density estimates in this area.

5.2 Conclusion
Accurate abundance estimates are vital for understanding where mitigation efforts
will be most effective for reducing anthropogenic threats such as cetacean-vessels
collisions and entanglement in derelict fishing gear. The abundance and density estimates
calculated in this thesis helps contribute to general understanding of humpback whale
seasonal variations within this study area. These estimates can aid future comparisons in
identifying changes in seasonal and yearly trends in distribution. While this study had its
limitations due to small sample size, I was able to generate conservative seasonal
abundance and density estimates of humpback whales off the coast of Washington and
Northern/Central Oregon between 2011-2012. Future studies should continue conducting
seasonal surveys along the US West Coast to obtain more sighting data, thus leading to
more robust estimates to determine seasonal use of this geographic region.

66

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