University of California, San Diego Differentiation Of

Home , Phocoena

University of California, San Diego

Differentiation of Narrow-Band High-Frequency Signals Produced by Odontocetes

A thesis submitted in partial satisfaction of the requirements for the degree Master of Science in

Oceanography

Grace Elizabeth Teller

Committee in charge:

Dr. Simone Baumann-Pickering, Chair Dr. Lisa Ballance Dr. John Hildebrand

2016

Grace Elizabeth Teller, 2016

The thesis of Grace Elizabeth Teller is approved, and it is acceptable in quality and form for publication

on microfilm and electronically:

______

______Chair

University of California, San Diego

2016

iii

TABLE OF CONTENTS Signature Page ………………………………………………………………………………………… iii

Table of Contents ……………………………………………………………………………………... iv

List of Figures …………………………………………………………………………………………... v

List of Tables ……………………………………………………………………………………………. vii

Acknowledgments …………………………………………………………………………………….. viii

Abstract of the Thesis .……………………………………………………………………………….. x

Section 1 Introduction ……………………………………………………………………….. 1 1.1 Odontocetes …………………………………………………………………… 1 1.2 Acoustics ………………………………………………………………………. 3 1.3 Distributions …………………………………………………………………. 7 1.4 Distribution Overlap ……………………………………………………… 15 1.5 Goal of Study …………………………………………………………………. 16

Section 2 Methods ……………………………………………………………………………... 18 2.1 Acoustic Recordings ………………………………………………………. 18 2.2 Acoustic Data Analysis …………………………………………………… 22 2.3 Click Type Discrimination ……………………………………………… 24 2.4 Click Type Analysis ………………………………………………………... 25

Section 3 Results ………………………………………………………………………………. 26 3.1 Click Type Cluster ………………………………………………………….. 26 3.2 Click Type Group …………………………………………………………… 30

Section 4 Discussion ………………………………………………………………………….. 35 4.1 Addressing Bimodal Inter-click Interval …………………………. 35 4.2 Comparison to Published Acoustic Recordings ……………….. 41 4.3 Kogia spp. Click Type ……………………………………………………... 40 4.4 Species Label Association ………………………………………………. 47 4.5 Spectral Properties Compared to Geographic Dominance ……………………………………………………………………. 50 4.6 Temporal Properties Compared to Geographic Dominance …………………………………………………………………… 51 4.7 General Geographic Association …………………………………….. 53

Section 5 Conclusion …………………………………………………………………………. 54

References ………………………………………………………………………………………………. 57

LIST OF FIGURES

Figure 1.1: Global distribution of harbor porpoise (Phocoena phocoena) ………………………………………………………….. 8 Figure 1.2: Harbor porpoise (Phocoena phocoena) distribution in the northeast Pacific Ocean ………………………………………...... 9 Figure 1.3: Distribution of Dall’s porpoise (Phocoenoides dalli) ……………………………………………………………. 10 Figure 1.4: Dall’s porpoise (Phocoenoides dalli) distribution in the northeast Pacific Ocean ………………………………………………………. 11 Figure 1.5: Global distribution of dwarf sperm whale (K. sima, top) and pygmy sperm whale (K. breviceps, bottom) ……………………. 13 Figure 1.6: Kogia spp. sightings based on shipboard surveys off California, Oregon and Washington, 1991-2008 …………………… 14

Figure 2.1: HARP schematic …………………………………………………………………. 19 Figure 2.2: Weekly number of hours with acoustic encounters of narrow-band high-frequency (NBHF) echolocation clicks ……... 21

Figure 3.1: Percent of each click type encountered at each site. ….…………... 27 Figure 3.2: ‘Left notch’ click types 1-4 …………………………………………………… 28 Figure 3.3: ‘Right notch’ click types 5-8 ……………………………………………..…... 29 Figure 3.4: Distribution of peak frequency per click type and group (colored boxes) …………………………………………………………..………. 32 Figure 3.5: Distribution of inter-click intervals (ICI) for ‘left notch’ click types 1-4. Values indicate ICI (ms) peaks based on Gaussian mixture models (GMMs) with 2 and 3(**) mixtures fitted (line) to the distribution …………………………………………….. 33 Figure 3.6: Distribution of inter-click intervals (ICI) for ‘right notch’ click types 5-8. Values indicate ICI (ms) peaks based on Gaussian mixture models (GMMs) with 2 and 3(**) mixtures fitted (line) to the distribution …………………………………………….. 34

Figure 4.1: Long term spectral average (LTSA) with spectrogram (top) and waveform (bottom) position underneath of a click type 2 encounter ……………………...……………………………………………………. 38 Figure 4.2: Long term spectral average (LTSA) with spectrogram (top) and waveform (bottom) position underneath of a click type 4 encounter ……………………...……………………………………………………. 39 Figure 4.3: Long term spectral average (LTSA) with spectrogram (top) and waveform (bottom) position underneath of a click type 7 encounter ……………………...……………………………………………………. 40

Figure 4.4: Median sound pressure spectrum level for encounters with narrow-band high-frequency signals in the Gulf of Mexico (blue line) followed the rise in energy spectra reported for click type 8 mean spectrum (black solid line) …....……………………. 45 Figure 4.5: Inter-click interval (ICI) distribution (gray bars) for encounters with narrow- band high-frequency signals in the Gulf of Mexico …………….……………………………………………………….. 46 Figure 4.6: Inter-click interval (ICI) plot for click type 8 (gray bars) overlaid with Gaussian mixture model curve generated from GofMX narrow- band high-frequency signal ICI’s (red line) …………………………… 46 Figure 4.7: Map of GofAK site with estimated depth and distance to shore and shelf break ……………………………………………………… 48 Figure 4.8: Map of DCPP (north site) and CINMS (south site) with estimated depth and distance to shore and shelf break ………… 49

LIST OF TABLES

Table 2.1: List of Deployments ……………………………………………………………. 20

Table 3.1: Total encounters of each click type per site ………………………….. 27 Table 3.2: Narrow-band high-frequency click parameter median and 10th to 90th percentile values for eight click types of unknown origin ……………………………………………… 31

Table 4.1: Results from three narrow-band high-frequency animals recorded in the wild, harbor porpoise & Dall’s porpoise, and in captivity, Kogia spp ……………………………………. 43 Table 4.2: List of potential species label (X) for each click type ……………... 44

vii

ACKNOWLEDGEMENTS

From the beginning where she painstakingly held my hand through learning how to write computer code, to the end where her poignant comments assisted in producing a completed thesis, I would like to thank my advisor, Simone Baumann-

Pickering, for all of the assistance, guidance, and motivation, she supported me with over the past 2+ years. I would also like to thank John Hildebrand for his perspective and quality time spent teaching me new techniques for achieving a clean data set, as well as, Lisa Ballance for her presence on my committee and insight about careers in the marine science field.

The staff in the SIO graduate office deserve a huge thanks for their administrative assistance and continued support. They are a big part of what makes

SIO an enjoyable and exciting place to be.

The support of the Behavioral Acoustic Ecology Laboratory, the Scripps

Whale Acoustics Laboratory, and the Marine Bioacoustics Laboratory has been invaluable. They created a warm place to accomplish goals, share ideas, and not feel overwhelmed by the amount of new information I had to absorb. Particular thanks to Beve Kennedy, Marie Roch, Sean Wiggins, Ana Širović, Ryan Griswold, Sean

Herbert, Jenny Trickey, Amanda Debich, Arial Brewer, Erin O’Neill, Bruce Thayre,

Anne Simonis, Kait Frasier, Katherine Cameron, Leah Lewis, Regina Guazzo, Eadoh

Reschef, Ashlyn Giddings, Anna Meyer-Löbbecke, and Camille Pagniello. I am especially thankful for my office mates Leah Varga and Ally Rice as they were always there to filter the slightly ridiculous questions and provide laughter in times of

viii stress. In particular, Leah Varga, who accompanied me along this journey to masters completion, for the constant reminder that I was not in this alone, and that it was ok to feel the pressure and let it out because “we can do it!”.

My cohort was wonderfully supportive. I am thankful for both their support of my scientific path and the social atmosphere they provided. They were an extremely motivating bunch of individuals who know how to take full advantage of every test, code, or whatever process that was still waiting to finish running.

I owe the most gratitude to my family and friends who were a huge source of confidence and strength thought this entire process. They prevented me from doubting myself for any length of time and reminded me to “let it go”, that everything will be ok, and that tomorrow is a new day. For that I would like to give my biggest thank you to Amy, Jim, and Sydney Green, Art Teller and Amy Stokes,

Cindi Weiss, Rose Teller, The Sprengelmeyer clan, Lindsay Hart, Kelsey Nielsen,

Hailey Brew, and Isabelle DeMillan. Lastly, my partner; in crime, on the dance floor, in the garden, and up the mountain you said was only a short distance, Mark

Moosburner. You were my rock this past year, bringing me back to earth when I was searching for answers in the atmosphere, I am eternally appreciative of you.

ABSTRACT OF THE THESIS

Differentiation of Narrow-Band High-Frequency Signals Produced by Odontocetes

Grace Elizabeth Teller

Master of Science in Oceanography

University of California, San Diego, 2016

Simone Baumann-Pickering, Chair

Passive acoustics are an excellent method for studying marine mammals through echolocation. The data captured in acoustic recordings has the ability to relay information about density, distribution, and behavior over long time periods.

The ability to understand, monitor, and predict these factors is imperative to the conservation and management of marine mammals. In order to utilize passive acoustics effectively, researchers must first become well acquainted with how to distinguish acoustic emissions.

The northeast Pacific Ocean is home to at least four species of odontocetes producing narrow-band high-frequency (NBHF) signals. Utilizing bottom moored passive acoustics these signals were recorded at three sites. Eight unknown click types of NBHF echolocation clicks resulted with varying spectral and temporal features that were used in discriminating harbor porpoise (Phocoena phocoena),

Dall’s porpoise (Phocoenoides dalli), and dwarf and pygmy sperm whales as a group

(Kogia spp). Associations were made based on relative dominance by click type at a site and known habitat and ecology of each species in question. Based on this association, harbor porpoise signals are best described by a notch position lower than the peak. Bimodal inter-click interval (ICI) below 40 ms and around 135 ms indicates a potential for this species to switch between long and short range sensing.

Possible Dall’s porpoise and Kogia spp. signals both maintain a spectral shape where the notch is positioned after the peak frequency. Distinguishing these species may best be done by the position of energy onset above and below 100 kHz and ICI around 40 ms or 80 ms, respectively.

Section 1

Introduction

1.1 Odontocetes

Cetaceans are fully aquatic marine mammals. They include the extant groups of odontocetes (toothed whales such as dolphins and porpoises) and mysticetes

(baleen whales) (Jefferson et al. 2015). Not only are cetaceans as a whole aesthetically pleasing, but also function as valuable indicator species for climate change (Moore 2008), marine protected areas (Hooker & Gerber 2004), and ecological risk assessments (Ross 2000). The impact that these organisms have in our oceans is beginning to become apparent, while not fully realized, we are beginning to understand the potential changes in nutrient cycling (Barlow et al.

2008, Roman & McCarthy 2010), and community compositions (Estes & Palmisano

1974, Jones et al. 1998, Ainley et al. 2006, Smith 2006) that may arise from the lack of conservation and management of cetaceans as a whole.

The historical efforts to monitor populations of marine mammals include aerial, ship, and land based survey techniques. Theses sampling techniques

2 all have caveats, as they require a surveyor’s presence and decent environmental conditions to be conducted. Even then, a sighting is not guaranteed as small group size, surfacing behavior, sea state, and visibility can result in potentially underestimated stock assessments and population trends or with big uncertainties

(Reeves et al. 2004, Taylor et al. 2007). Advances in monitoring techniques allow scientists to complement the aforementioned methods with an alternative; passive acoustic monitoring. While passive acoustics have their own set of limitations, such as the instrument failure or the data having to be recovered from the instrument

(Mellinger 2007), it does have the ability to record marine mammal signals over periods of day and night, during all weather conditions, at remote and difficult of access locations, without a surveyor present, and can more easily capture the presence of those species not readily seen visually at the surface (Mellinger et al.

1996, Wiggins & Hildebrand 2007, Schaffeld et al. 2016). These reasons make the use of passive acoustics a good tool for monitoring marine mammals.

Odontocetes produce echolocation clicks that are used in spatial orientation and food acquisition (Au 1993). Echolocation signals can be recorded and analyzed to determine density, distribution, and behavior of cetaceans over time (Laiolo

2010, Hildebrand et al. 2015, Kracker 2015, Todd et al. 2016, Schaffeld et al. 2016).

The conservation and management of cetaceans relies on our understanding of these factors (Krouse & Farina 2016, McDonald et al. 2016). Passive acoustics are one way researchers can begin to ascertain the information necessary for effective conservation and management of marine mammals. However, before utilizing

3 acoustics to grapple with quantifying populations or analyzing behaviors, researchers must first be able to differentiate species acoustically (Mellinger &

Barlow 2003).

Passive acoustics have been utilized in discriminating sounds produced by marine mammals from other biota and anthropogenically produced noise (André et al. 2011), for classifying signal encounters to species (Oswald et al. 2007, Soldevilla et al. 2008), and in discriminating multiple signals belonging to a single species

(Stimpert et al. 2011, Baumann-Pickering et al. in press, Reyes et al. 2015). All of these studies utilize specific features of the signal detected in order to discriminate and classify their target species. The goal of this study is to decipher which features of the signal accomplish this task most proficiently for discriminating the narrow- band high-frequency (NBHF) signals produced by porpoises and Kogia spp.

(Hatakeyama & Soeda 1990, Au et al. 1999, Madsen et al. 2005, Miller & Wahlberg

2013).

1.2 Acoustics

Acoustic differentiation of species can be challenging when the animals emit very similar sounds. Harbor porpoise (Phocoena phocoena), Dall’s porpoise

(Phocoenoides dalli), and the pygmy sperm whale (Kogia breviceps) all display NBHF echolocation signals with similarities in temporal and spectral features that make them difficult to differentiate using passive acoustics (Hatakeyama & Soeda 1990,

Au et al. 1999, Madsen 2005, Miller & Wahlberg 2013). Currently, nothing is known

4 about the signal production of the dwarf sperm whale, but due to similarities to their sister species, pygmy sperm whale, they likely possess similar echolocation capabilities.

Echolocation clicks are highly directional with most energy being emitted on the central axis of the echolocation beam (Au 1993). Many studies describing click features focused on the analysis of on-axis clicks for signal description, since on-axis is the angle perceived by the animals and optimized for echolocation purposes (Au

1999). In a passive acoustic monitoring context, signals from all angles relative to the echolocation beam are recorded and considered for signal descriptions

(Soldevilla et al. 2008, Baumann-Pickering et al. 2015). Features of echolocation clicks typically published are peak frequency, center frequency, inter-click interval

(ICI), duration, -3db bandwidth, and source level. Peak frequency is the frequency in a spectrum with the highest received level. Center frequency is the integral midpoint of the click spectrum. ICI is the time in between the start of one click and the start of the previous click. Duration is the time between the start and end of a click. -3dB bandwidth is the frequency range of the signal less 3dB from spectrum peak. Source level is the energy produced by the animal at the time of signal emission. These spectral and temporal features provide quantifiable properties of the signal, by concentrating on specific parts of the signal relevant information can be omitted regarding frequency peaks and notches in the spectrum and overall shape of the spectrum.

Harbor porpoise (Phocoena phocoena)

Harbor porpoise echolocation has been studied both in captivity and in the wild. Studies with captive animals have the ability to position the subject directing their directional echolocation beam towards the recording hydrophone way to optimally capture on-axis echolocation clicks and define their spectral and temporal properties as well as transmission beam patterns in captivity (Møhl & Anderson

1973, Au 1999, DeRuiter et al. 2009, Verfuss et al. 2009, Koblitz et al. 2012). This method may not be fully representative of echolocation in the wild.

Studies of wild harbor porpoises using passive acoustics have occurred widely within their geographic distribution (Akamatsu et al. 2007, Villadsgaard et al.

2007, Clausen et al. 2011, Miller & Wahlberg 2013). In inner Danish waters

Villadsgaard (2007) defined the echolocation signals as having an ICI ranging from

30 to 200 ms with a mean of 60 ms, a duration of 44 to 113µs, a center frequency of

130 to 142 kHz, and a peak frequency of 129 to 145 kHz. In the wild, harbor porpoise echolocation signals from were described as having a mean ICI of 80 ms while continuously sensing their environment – search phase – and reducing to less than 20ms when approaching a target - approach phase (Akamatsu et al. 2007).

Additional evidence of a search and approach phase (Verfuss 2005) and an approach phase (DeRuiter 2009) has been described, mirroring the trend in decreasing ICI value upon approach described by Akamatsu (2007). This is not a novel echolocation concept as bats, who also depend on echolocation for sensing their environment and finding prey, have a similar echolocation strategy. They

6 switch from long ICIs during the search for prey to continuously shorter ICIs while moving in on a target (Madsen & Surlykke 2013). Clausen (2011) and colleagues further studied descriptors of harbor porpoise NBHF signals regarding communication and discovered strong evidence for the presence of specific patterns of clicks relating to communication between different sex, age, and relationship status of the species. In summary, harbor porpoises appear to have the ability to cover a range of click rates dependent on their current behavioral state.

Dall’s porpoise (Phocoenoides dalli)

Dall’s porpoises are challenging to capture and maintain alive for an extended period of time, resulting in minimal descriptions of their acoustic emissions in captivity (Hatakeyama & Soeda 1990).

Awbrey (1979) and colleagues described Dall’s porpoise echolocation in the wild. The NBHF signal was found as having a peak frequency between 120 and 160 kHz, a duration of 50 to >1000µs, and an ICI of 13 to 143 ms. A more recent study of wild Dall’s porpoises found that the NBHF signal was described by a peak frequency of 135 to 149 kHz, a duration of 50 to 60 µs, and an ICI of 8 to 150ms (Hatakeyama

& Soeda 1990).

Dwarf and Pygmy sperm whale (Kogia sima, K. breviceps)

Dwarf and pygmy sperm whales, both of genus Kogia, are very challenging to locate in the wild because they occupy deep, pelagic waters, occur solitary or in small groups, conduct extended deep dives with short surface intervals, have near- invisible blows, and maintain a low profile at the water’s surface (McAlpine 2002,

Jefferson et al. 2015) . When encountered at sea, an identification to the species level is often not possible, there is no published concurrent visual sightings and acoustic recordings of either species at sea. Hence, all available information on acoustic properties of Kogia spp. is based on studies of alive stranded Kogia breviceps recorded once washed ashore or later in captivity (Marten 2000, Ridgway & Carder

2001, Madsen et al. 2005). The signals recorded were presumed to be used for echolocation, displaying a frequency range from 60 to 200 kHz, peaking around 125 kHz, lasting on average 119 µs with an ICI varying between 40 and 70 ms (Marten

2000, Madsen et al. 2005). One encounter with a wild Kogia sima confirmed that the acoustic characteristics are similar (V. Janik, unpublished data). Similarities in the physical attributes of the skull may account for a similar production of acoustic signals (McAlpine 2002). For the purpose of this study, pygmy and dwarf sperm whales will be referred to as Kogia spp. due to the uncertainty in distinguishing these species in the wild and the lack of acoustic information present for both species.

1.3 Distributions

Distinguishing NBHF signals generated by porpoises and Kogia spp. in the

North Pacific is further hindered by the potential for habitat overlap. (Willis et al.

2004, Caldwell & Caldwell 1989, Ohizumi et al. 2003, Baumgartner et al. 2001,

Douglas et al. 2014, De Boer et al. 2014, Willis & Baird 1998) .

Harbor porpoise (Phocoena phocoena)

Harbor porpoises have been observed to inhabit shallow coastal waters, 100 m or less (Barlow 1988) (Figure 1.1). In the eastern North Pacific Ocean, they have been found to inhabit coastal waters in the Gulf of Alaska (Dahlheim et al. 2000) all the way down the coast of California (Gaskin 1984) (Figure 1.2). These sightings showcase minimal movement patterns in both of these areas based on distinct signatures of chlorinated hydrocarbon (Calambokidis & Barlow 1987).

Figure 1.1: Global distribution of harbor porpoise (Phocoena phocoena). (from NOAA/NMFS, http://www.nmfs.noaa.gov/pr/species/mammals/cetaceans/harborporpoise.htm)

Figure 1.2: Harbor porpoise (Phocoena phocoena) distribution in the northeast Pacific Ocean. Approximate distribution of harbor porpoise in Alaska waters (shaded area) (top). Stock boundaries and distributional range of harbor porpoise along California and southern Oregon coast. Dashed line represents harbor porpoise habitat (0-200m)(bottom). (from NOAA/NMFS, http://www.nmfs.noaa.gov/pr/sars/pdf/stocks/alaska/2014/ak2014_harborporpoise-goa.pdf and http://www.nmfs.noaa.gov/pr/sars/2013/po2013_harborporpoise-ncasor.pdf)

Dall’s porpoise (Phocoenoides dalli)

Dall’s porpoises, conversely, inhabit more pelagic waters from the continental shelf, starting around 200 m, to open ocean (Morejohn 1979)(Figure

1.3). In the eastern North Pacific Ocean, Dall’s porpoises are present year round in the Gulf of Alaska (Loeb 1972, Leatherwood & Fielding 1974) and display continuous presence down the coast showing some signs of seasonal migration up and down the coast (Green et al. 1993, Mangels & Gerrodette 1994, Barlow 1995,

Forney 1995) (Figure 1.4). The southern end of the Dall’s porpoises’ geographic range is not well documented, but sightings near the Mexican border have occurred during times when cold water was present (Barlow 1995, Forney 1995, Mangels &

Gerrodette 1994).

Figure 1.3: Distribution of Dall’s porpoise (Phocoenoides dalli). (from NOAA/NMFS, http://www.nmfs.noaa.gov/pr/species/mammals/cetaceans/dallsporpoise.htm)

Figure 1.4: Dall’s porpoise (Phocoenoides dalli) distribution in the northeast Pacific Ocean. Approximate distribution of Dall’s porpoise in Alaska waters (shaded area) (top). Dall’s porpoise sightings from aerial and shipboard surveys off California, Oregon, and Washington, 1991-2008. Dashed line represents the U.S. EEZ and thin lines represent completed transect effort of all surveys combined. (courtesy of NOAA/NMFS http://www.nmfs.noaa.gov/pr/pdfs/sars/ak2012poda.pdf and http://www.nmfs.noaa.gov/pr/pdfs/sars/po2010poda-cow.pdf)

Kogia spp.

Kogia spp. are challenging to monitor through visual surveys because they do not have a distinct presence at the surface and are difficult to distinguish at sea. The individual species’ distribution has been largely derived by the presence of stranded animals (Willis & Baird 1998, Wursig et al. 2000, Jefferson et al. 2015) . Across their wide distribution (Figure 1.5) they are likely located in deep areas around the continental shelf and slope and occasionally over the abyssal plain (Fiedler et al.

1990, Baird et al. 1996, Jackson et al 2004, Baird 2005). Current information on

Kogia spp. does not provide species-specific distribution patterns, but evidence is present for foraging at depth and habitat overlap of both Kogia spp. (Willis & Baird

1998, Staudinger et al. 2014). Few sightings of poor confidence for species identification have occurred in the eastern North Pacific Ocean (Figure 1.6, NOAA

2010).

Figure 1.5: Global distribution of dwarf sperm whale (K. sima, top) and pygmy sperm whale (K. breviceps, bottom). (from NOAA/NMFS, http://www.nmfs.noaa.gov/pr/species/mammals/cetaceans/dwarfspermwhale.htm and http://www.nmfs.noaa.gov/pr/pdfs/sars/po2010whpy-cow.pdf)

Figure 1.6: Kogia spp. sightings based on shipboard surveys off California, Oregon and Washington, 1991-2008. Dashed line represents U.S. Exclusive economic zone and thin lines indicates completed transect effort of all surveys combined. (from NOAA/NMFS, http://www.nmfs.noaa.gov/pr/pdfs/sars/po2010whds-cow.pdf)

1.4 Distribution Overlap

In general, food availability, seasonality, and/or temporal changes in the local environment can have the potential to impact the movement of an individual (Willis

& Baird 1998, Baumgartner et al. 2001, De Boer et al. 2014, Douglas et al. 2014).

Movement patterns and demonstrated geographic distribution afford the ability for species of this study to have some degree of overlap as they are crossing paths during transit, arriving at the same destination, or co-occurring for an extended period of time. Commonalities in habitat area, target prey, and behavior of some species yield more potential for overlap than others.

Harbor and Dall’s porpoise

Visual surveys of distribution confirm some degree in habitat overlap by harbor and Dall’s porpoise. These species may compete for resources as they may consume similar, small schooling fish as prey (Recchia & Read 1989, Spitz et al.

2006, Walker 1996). The Dall’s porpoise does take advantage of their deeper habitat by also feeding on squid and mesopelagic fishes that rise to surface waters at night (Walker 1996). In addition to potential consumption of similar prey items and overlap in depth distribution, harbor and Dall’s porpoise have been found to hybridize, suggesting that some co-occurrence is possible and likely (Willis et al.

2004).

Harbor porpoise and Kogia spp.

The known behaviors of these species are very different from one another.

Harbor porpoise reside in shallow water up to 200 m depth, feeding on small

16 schooling fishes (Recchia & Read 1989, Spitz et al. 2006), while Kogia spp. are pelagic in nature and forage likely at depth, consuming mostly cephalopods

(Staudinger et al. 2014, Willis & Baird 1998). The severe discrepancies in habitat use and target prey reduce the possibility for these animals to occur in the same area for any length of time.

Dall’s porpoise and Kogia spp.

Dall’s porpoise and Kogia spp. display similar preference for deep water and potential similarities in deep foraging behavior, increasing the potential for these species to be present across the same area (Caldwell & Caldwell 1989, Ohizumi et al.

2003). Both species forage on cephalopods and various fishes, in different abundances (Walker 1996, Staudinger et al. 2014). The general lack of knowledge surrounding Kogia spp. creates doubt in estimating the amount of habitat overlap between it and porpoises as habitat ranges may differ from current predictions.

However, the depth distribution yields ample room for potential overlap of these species, both are present in pelagic waters (Morejohn 1979, Fiedler et al. 1990,

Baird et al. 1996, Jackson et al 2004, Baird 2005).

1.5 Goal of Study

The current little knowledge on NBHF signals produced by harbor porpoise,

Dall’s porpoise, and Kogia spp. results in a need to further examine echolocation signals they may produce for the purpose of passive acoustic monitoring and species classification based on echolocation. The potential degrees of habitat

17 overlap presented by these four species increases the challenge in determining species associations to signals produced in the northeastern Pacific Ocean. The purpose of this study is to discriminate wild harbor porpoise, Dall’s porpoise, and

Kogia spp. by quantifying NBHF signals detected at three sites. To carry out this study, passive acoustic monitoring techniques were utilized in the Gulf of Alaska and

Southern California. The ability to quantify these NBHF signals will provide an avenue for increasing scientific understanding and expanding conservation and management practices of these four species.

Section 2

Methods

2.1 Acoustic Recordings

Autonomous high-frequency acoustic recording packages (HARPs) (Wiggins

& Hildebrand 2007, Figure 2.1) were bottom-moored at four sites, of which two were in Southern California, one in the Gulf of Alaska, and one in the Gulf of Mexico.

The hydrophone was positioned about 10 m off the seafloor. The Southern

California HARPs were located off Point Conception in the Channel Islands National

Marine Sanctuary (CINMS, 34˚19.5’ N, 120˚48.4’ W) and off shore from San Simeon in the Diablo Canyon Power Plant (DCPP, 35˚36.7’ N, 121˚14.5’ W). The Gulf of

Alaska HARP was positioned south of Seward near the shelf (GofAK, 59˚00.7’ N,

148˚54.2’ W), and the Gulf of Mexico HARP was positioned off shore of Louisiana on the shelf break (GofMX, 28˚50.7’ N, 88˚27.9’ W). There were multiple deployments from September 2011 to August 2013 with varying recording effort (Table 2.1,

Figure 2.2).

Figure 2.1: HARP schematic. HARP Seafloor package including data logger and acoustic release electronics pressure cases, ballast weight, glass flotation sphere in yellow hard hats, and hydrophone tethered ~ 10m above seafloor. (Wiggins & Hildebrand 2007)

06:20

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in the Gulf in Alaskaof (GofAK,59˚00.7’ N,

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Figure 2.2: sites For GofAK, DCPP, and CINMS (black andbars) percentage effortof per week continuousrecording effort

HARPs were set to a sampling frequency of 320 kHz with 16-bit quantization and recorded continuously. The recorders were equipped with an omni-directional sensor (ITC-1042, International Transducer Corporation), which had an approximately flat (+/- 2dB) frequency response from 10 Hz to 150 kHz with a hydrophone sensitivity of -200 dB re V/µPa. The sensor was connected to a custom- built preamplifier board and band-pass filter. The calibrated system response was accounted for during analysis.

2.2 Acoustic Data Analysis

Recordings were processed using the MATLAB (Mathworks, Natick MA) based custom software program Triton (Wiggins & Hildebrand 2007) and other

MATLAB custom scripts. Long-term spectral averages (LTSAs) were calculated for visual analysis of the long-term recordings. LTSAs are long-term spectrograms with each time segment consisting of an average of 500 spectra, calculated over 5s time bins averaging over 100Hz within each bin, with a fast Fourier transform (FFT) of

3200 (Welsh 1967). .

A trained analyst visually screened 1 hour segments of the LTSAs and manually recorded where high frequency echolocation clicks were present for a few deployments of DCPP and CINMS (Table 2.1, Index 1-5). A subset of these manually recorded times were later used to compare against the automated detector output as defined below to determine detector performance. Echolocation clicks were deemed narrow-band high-frequency (NBHF) when all the energy of the click was

23 above 80 kHz. Start and end times of an acoustic encounter were logged. Acoustic encounters were defined as a series of echolocation clicks with similar spectral properties and separated by than 20 minutes between click series.

Individual signals were automatically detected using a Teager-Kaiser energy detector (Soldevilla et al. 2008, Roch et al. 2011). Detected clicks were digitally filtered with a 10-pole Butterworth bandpass filter with a bandpass between 95 and

155 kHz. Filtering was done on 1300 sample points centered on the detected signal.

Spectra of each detected signal were calculated using 3.2 ms (1024 samples) of

Hann-windowed data centered on the signal. Signal parameters peak frequency, center frequency, -3dB bandwidth, and sound pressure spectrum (dB re 1 uPa2/Hz) were processed using methods from Au (1993). Click duration and inter-click interval (ICI) were a result of the detector output. Peak-to-peak (pp) click received levels were calculated from the waveform amplitudes and adjusted for the system response at the center frequency of each click.

Subsequently, all automatic detections with a peak frequency below 100 kHz were eliminated, leaving predominantly NBHF clicks. To further eliminate periods with false detections, statistics were run on 75-s segments, the HARP raw data format, making this a convenient time increment, and a set of criteria were defined.

There had to be at least 3 clicks per segment after elimination of peak frequency below 100 kHz. Additionally, at least 40% of all initial clicks in a segment needed to remain after deletion for it to be considered as containing NBHF clicks.

Another clean-up step was taken to manually remove sporadic false detections, echoes, or instrument noise. An analyst-guided MATLAB software tool displayed LTSAs over the length of an acoustic encounter with corresponding pp received levels and ICIs for each detected signal. Individual signals could be marked to display their spectrum and waveform in comparison to a mean spectrum and mean waveform of all detections within the acoustic encounter. False detections were manually marked and automatically deleted. Lastly, signals were removed with pp received levels below 125 dB re 1µPa or containing a -3 dB bandwidth greater than 30 kHz, both indicating a low signal-to-noise ratio and resulting in problematic signal descriptions. Mean spectra were calculated over all remaining signals of each acoustic encounter.

2.3 Click Type Discrimination

Mean spectra of each acoustic encounter from all sites were visually scanned to determine the most prevalent spectral shapes. Discriminations were based on the position of the click energy onset, spectral notch positions, and peak frequencies.

Sorting of these mean spectra per acoustic encounter to a click type category resumed in an iterative process until each type contained a homogenous group of mean spectral shapes. The Kogia spp. click type was derived from GofMX recordings.

The iterative sorting process was such that a mean spectrum that closely resembled one of the click types was labeled as that click type. A mean spectrum that did not closely resemble relevant features of a click type was classified as unknown.

Multiple unknowns with similar features were grouped as a new click type. This process was repeated until all mean spectra were assigned to a click type. 10th % and 90th % values were computed for each click type.

Since autonomous acoustic recordings do not provide visually confirmed information about the species being recorded, another method for associating species labels with the newly defined click types was necessary. Click types were examined in relation to the dominance of that click type at a site. Site depth, proximity to the coast, and proximity to the shelf break were used in appropriating known habitat use to individual species.

2.4 Click Type Analysis

Inter-click intervals per click type were evaluated for single or multi-modal distribution. Gaussian mixture models (GMM) with two or three mixtures were computed depending on the click type. The GMM resulted in several modes, one model per mixture. The peak value for each primary and, if appropriate, also secondary mode was extracted and, used in comparing ICI modes of click types to one another.

Section 3

Results

3.1 Click Type Clusters

Quantities of each click type varied at each site (Table 3.1, Figure 3.1). Mean spectra calculated across all clicks of each acoustic encounter were visually compared against each other and separated into eight click types (Figure 3.2 & 3.3).

Each click type displayed some variation in spectral structure. However, the spectrum did reveal key features required for categorizing each mean spectrum to a click type. Notch position and frequency value of energy onset were two features derived from the spectra that allowed for categorizing click types further into two clusters.

‘Left Notch’ (click types 1-4, Figure 3.2) or ‘Right Notch’ (click types 5-8,

Figure 3.3) clusters were determined by the position of the notch. The appearance of the notch before or after the peak in spectra indicated a left or right notch click type, respectively. A notch is described by a noticeable dip in spectral values before returning to the increasing or decreasing trend depicted prior to the notch. The start position of the energy onset was utilized to better distinguish click types within the cluster (Figure 3.2, 3.3, & Table 3.1). The ‘left notch’ cluster showcased energy onset between 110 and 117 kHz marked by a notch position between 127 and 128 kHz, and a peak frequency around 143 kHz. Whereas, the right notch cluster displayed an energy onset between 90 and 115 kHz marked by a notch position around 149 kHz, and a median peak frequency around 135 kHz.

Table 3.1: Total encounters of each click type per site.

Site Click Types 1 2 3 4 5 6 7 8 GofAK -- -- 2 -- -- 2 5 -- DCPP 444 64 121 160 4 3 28 1 CINMS 1 ------16 22 -- 3 Total 445 64 123 160 20 27 33 4

Figure 3.1: Percent of each click type encountered at each site. Color indicates which group the click type belongs to. Where group is based on similarities or differences in spectral and temporal properties

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3.3 Click Type Group

Clusters were further divided by peak frequency and inter-click interval

(Table 3.2, Figure 3.4). The median values calculated for peak frequency of all clicks within a click type complement the distinction of the ‘left notch’ and ‘right notch’ clusters (Figure 3.4), represented by values above and below 139 kHz, respectively.

Gaussian mixture models (GMMs) with two or three mixtures were produced modal values for inter-click interval (ICI). These modes were utilized to further divide the clusters into two groups each. The highest peak in ICI distribution was used for comparison (Figure 3.5 & 3.6). The ‘left notch’ cluster was grouped by the presence of a high peak in the ICI distribution that was either greater or less than 40 ms. This separated click types 1, 2, and 3 into one group and click type 4 into another within the ‘left notch’ cluster. The ‘right notch’ cluster was further grouped by the presence of a high peak in the ICI distribution that was either greater or less than 55 ms. Click types 5, 6, and 7 comprised the group and click type 8 was distinguished as a separate group within the ‘right notch’ cluster.

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Section 4

Discussion

4.1 Addressing Bimodal Inter-Click Interval

Gaussian mixture models produced a second peak in inter-click interval (ICI) values for click types 2, 4, and 7 (Figure 4.1 & 4.2). The bimodality described by this second peak can be explained by a change in the rate of echolocation transmission of one animal or by the presence of other animals being detected. Some bias may result when every other signal of an acoustic encounter is intermittently not detected as the ICI is calculated by the difference in time from the start of a signal and the start of a signal before. Should a signal be omitted from detection an ICI value would be created for e.g., the first and third signal detected resulting in an inaccurate ICI value (Figure 4.1).

Click type 2 demonstrated a second peak in ICI at 136 ms (Figure 3.7). The bimodality of this click type was not an artifact of detector performance, although in

36 the example sequence one missed detection occurred that contributed to the second

ICI peak. The majority of detections within the two modes were an actual indication of a behavioral change of the echolocating animal (Figure 4.1). An alternative pattern was found for click type 4 as the second peak in ICI is of shorter duration,

49ms, than the main peak in ICI, 135ms (Figure 3.5). This click type was also truly bimodal and showed two preferred ICIs for echolocation (Figure 4.2). In this case, the main peak ICIs were found with intermittent periods of fast ICIs. The presence of a lower amplitude signal occurring within the high peak ICI range for this click type may be caused by the presence of a second animal. However, this is unlikely as a lower amplitude signal is neither encountered before or after this short period of time. If both of these click types are produced by the same animal, then the bimodality was dependent on the behavioral state of the animal.

Comparatively, click type 7 displayed a wide range of ICI values with the second peak at 143ms, almost as high as the main peak at 51ms (Figure 4.1). The wide distribution of ICI values may have occured due to the presence of a second animal being recorded. The presence of a second animal may influence the overall distribution of ICI, however the main peak of this click type is still comparable to click types 5 and 6 where a high peak in ICI occurs around 40 ms (Figure 4.4).

Narrow-band high-frequency (NBHF) signals produced by cetaceans clearly display their ability to change the rate of signal production. However, the impact on overall ICI distributions of missed detections and the presence of a second animal being recorded was not determined. The second peak ICI value was not considered

37 for species classification, but does indicate the interesting potential of long and short range sensing by these species (Nelson & Maclver 2006, Akamatsu et al. 2010).

The high peak ICI had the most detections and was used to further signal discrimination.

the

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ngterm spectral average (LTSA

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4.2 Comparison to Published Acoustic Recordings

The results of this study provide useful information for quantifying narrow- band high-frequency (NBHF) signals produced in the Gulf of Alaska and off the coast of southern California. Peak frequency, inter-click interval (ICI), energy onset, and notch position relay quantifiable material for examining click types by cluster, group, and individually. The spectral and temporal values produced by this study

(Table 3.2, Figure 3.2 & 3.3) are utilized in conjunction with references to previous recordings (Table 4.1), and percentage of click types encountered at each site

(Figure 3.1) for potential species discrimination (Table 4.2). Previously reported values for harbor and Dall’s porpoise, and Kogia spp. fall within similar ranges and provide little discriminatory value (Table 4.1). The values do indicate that these animals emit NBHF echolocation signals. The NBHF signal values of these species described in literature supports the analysis and discrimination of NBHF signals in the Gulf of Alaska, southern coast of California, and utilizing NBHF signals as a spectral template and comparison for Kogia spp. from the Gulf of Mexico. Peak frequency and ICI previously reported for all species fall within the range of peak frequency and ICI values presented in this study (Awbrey 1979, Hatakeyama &

Soeda 1990, Marten 2000, Madsen 2005, Akamatsu et al. 2007, Villedsgaard et al.

2007, DeRuiter et al. 2009).

Methodology may account for some variation in value comparison as prior studies selected for apparent on-axis signals reducing variability when compared to this study, which included both on and off-axis signals. The amount of variability

42 found in signal detections can have a large effect on reported values. Measurements derived from the waveform of a signal rely on the accuracy of that waveform. Should a waveform be altered from the behavioral state of the animal (Kloeper et al. 2012), proximity of the animal (Urick 1983), and/or angle of transmission (Au et al 1995,

2012), the resulting measurement will also be altered. The presence of a second animal echolocating within range for detection can further hinder measurements relating to the timing of echolocation, such as ICI. Regardless of any of the aforementioned variables for extracting measurement the animals in this study were likely recorded within a ~1km radius of the instrument due to reported source levels and frequency content. The close proximity of an animal required to record a signal may increase the accuracy of the measurements derived from the signal (Au et al. 1999).

Comparison of peak frequency and ICI from literature yields little explanatory value for species discrimination by their echolocation clicks. Thus, percentage of click types encountered at each site compared to spectral and temporal properties found in this study may better distinguish the species, elaborated below.

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Table 4.2: List of potential species label (X) for each click type. Species label derived from association of percentage of click type encountered at site to known habitat ranges and animal behavior. Click Type

Species Label 1 2 3 4 5 6 7 8 Harbor Porpoise X X X X ------(Phocoena phocoena) Dall’s porpoise -- -- X -- X X X X (Phocoenoides dalli) Kogia spp. ------X (breviceps, sima) ------X ------X Other

4.3 Kogia spp. Click Type

Visual surveys suggest that no porpoises or delphinids that produce NBHF signals are present in the Gulf of Mexico (GofMX), thus Kogia spp. are assumed to be the sole producer of NBHF signals in the GofMX (Richardson et al. 2013,

Baumgartner et al. 2001, Davis et al. 2002). Echolocation clicks with NBHF properties were analyzed for their spectral and temporal features from the GofMX for Kogia spp. Since no other dolphins or porpoises were known to produce NBHF clicks that resided in the gulf, all encounters with NBHF signals were presumed to belong to Kogia spp. Past records of Kogia spp. signals were presumed to be used for echolocation, displaying a frequency range from 60 to 200 kHz, peaking around 125 kHz, lasting on average 119 µs with an ICI varying between 40 and 70 ms (Marten

2000, Madsen et al. 2005). Comparison of mean spectra from NBHF echolocations clicks detected in the GofMX to the spectral features of NBHF click types from the eastern North Pacific indicated that click type 8 was potentially Kogia spp. (Figure

4.4). The estimated energy onset and notch position for GofMX were 91.6 kHz, and

143.1 kHz, respectively. These values came within 5-6 kHz of the reported values for click type 8. The overall rise in energy displayed by the GofMX site complemented the energy rise of click type 8. Most of the spectral structure found for click type 8 fall within close proximity to those found at the GofMX site.

Figure 4.4: Median sound pressure spectrum level for encounters with narrow-band high- frequency signals in the Gulf of Mexico (blue line) followed the rise in energy reported for click type 8 mean spectrum (black solid line). The onset of energy was ~90-95 kHz (red bold solid line) and the notch is located at ~140-150 kHz (green star). Most of the spectral properties displayed by click type 8 were very similar to the template GofMX data.

Due to the low sample size of click type 8 encounters, GofMX data was used to create Gaussian mixture models for ICI (Figure 4.5). These values were overlaid on the click type 8 ICI histogram for further comparison (Figure 4.6). The peak in ICI calculated through the Gaussian mixture model for GofMX data complemented the peak in ICI distribution found for click type 8.

Figure 4.5: Inter-click interval (ICI) distribution (gray bars) for encounters with narrow-band high-frequency clicks in the Gulf of Mexico. Three mixture Gaussian mixture models revealed a high peak in ICI distribution at 83 ms (red line).

Figure 4.6: Inter-click interval (ICI) distribution for click type 8 (gray bars) overlaid with Gaussian mixture model curve generated from GofMX narrow-band high-frequency signal ICI’s (red line). The peak in ICI displayed by click type 8 falls near the peak in GofMX data’s ICI around 83 ms.

4.4 Species Label Association

The presence of a click type at a site was used in conjunction with temporal and spectral properties to determine a possible species label (Figure 3.1, Table 4.2).

The GofAK site was at a depth of 200m with a distance of 80km from shore and

51km from the shelf break (Figure 4.7). Click type 7 was encountered 56% of the time and click types 3 and 6 were each encountered 22% of the time, 78% of clicks detected at this site belong to the green group (click types 6 and 7, Figure 3.1).

GofAK site is located in Dall’s porpoise habitat, but harbor porpoise cannot be ruled out as they may frequent these waters. Kogia spp. are not known to be found at this high of latitude.

The shallowest site was DCPP at a depth of 65m, it was also the closest to shore, 5km, and farthest from shelf break at 71km (Figure 4.8). Click type 1 was most abundant here, encountered 54% of the time followed by click types 3 and 4,

15% and 19%, respectively. Click types belonging to the purple group (click types 1-

3) were most prevalent at this DCPP, encountered 77% of the time (Figure 3.1). The shallow depth of this site is ideal for harbor porpoise, which has been visually documented at this site (Barlow 1988, Jacobson et al. 2016). The deep foraging nature of Dall’s porpoise and Kogia spp. make their presence in shallow water less likely. CINMS site was the deepest site at 800m with a shorter distance to shore,

34km, than the GofAK site and a shorter distance to the shelf break, 25km, than both

GofAK and DCPP (Figure 4.8). Click type 6 was encountered most often at 52%, followed closely by click type 5 at 38% (Figure 3.1). Similarly to GofAK site, CINMS

48 was comprised mostly of click types belonging to the green group. The deep water and close proximity to the shelf break at CINMS site could be adequate habitat for both Dall’s porpoise and Kogia spp. Harbor porpoise is likely absent in this area due to the distance from shore and water depth.

80km

200m

51km

Figure 4.7: Map of GofAK site with estimated depth and distance to shore and shelf break.

65m 5km

71km

34km 800m

25km

Figure 4.8: Map of DCPP (north site) and CINMS (south site) with estimated depth and distance to shore and shelf break.

4.5 Spectral Properties Compared to Geographic Dominance

Spectral properties, overall shape, position of notch, position of energy onset, and peak frequency have been used in distinguishing many species with similar acoustic qualities. When spectral properties are present they can relay species- specific information used for classification of echolocation to species label

(Soldevilla et al. 2008, Baumann-Pickering et al. 2015). The ability to classify an animal using spectral properties can more confidently be accomplished when researchers are able to connect visual observation to the timing of echolocation emission. The connection of signal to a singular species can be accomplished by the placement of a tag on the species in question (Baumann-Pickering et al. 2015), but confidence is increased when the presence of other species nearby is ruled out

(Soldevilla et al. 2008). In this study neither methods were implemented, thus the association of signal produced to species label can only be speculated. Variation in reported spectra based on the 10% and 90% calculated values is likely a factor of the natural flexibility of a signal emitted.

Notch and Energy Onset Positions

Notch and energy onset position reported from the spectra serve as good primary and secondary qualifiers of the signal produced, respectively. Right and left notch clusters are present at all sites in varying quantities. Right notch clusters dominate at the sites of greater depth and of closer proximity to shelf break, GofAK and CINMS. Whereas, the left notch cluster is found to dominate at the shallow,

51 coastal site, DCPP. Energy onset is relatively uniform across the left notch cluster and serves no purpose for further click type differentiation. However, the lower value reported for click type 8 energy onset yields further discrimination for the right notch cluster. The majority of click type 8 signal encounters occurred at a right notch cluster dominant site, CINMS, this click type cannot yet be ruled distinct by this factor alone. As this click type also belongs to the right notch cluster it is possible that this click type is a variation in signal production from the same species producing the rest of the click types, 5, 6, and 7, in the right notch cluster.

Peak Frequency

Distribution of peak frequency produced by an animal has been used to discriminate between species (Matthews et al. 1999), populations within species

(Castellote et al. 2012), and variations of call type within a species (Rasmussen &

Miller 2002, Dunlop et al. 2007 , Villadsgard et al. 2007, DeRuiter et al. 2009). Here peak frequency clearly supports the division by notch position (Figure 3.4). The range in peak frequency of the left notch cluster is noticeably higher than that of the right notch cluster. Within the left notch cluster multiple click types with the same peak frequency distribution likely belong to the same species. Within the right notch cluster peak frequency distribution may not be as decisive parameter for species discrimination.

4.3 Temporal Properties Compared to Geographic Dominance

ICI can be an informative value for species discrimination, especially when the species in question echolocate at a similar peak frequency (Baumann-Pickering et al. 2013). ICI has been shown to change based on the animals behavior (Au 1993,

Simard et al. 2010, Clausen 2011), and between species (Baumann-Pickering et al.

2013). ICI further differentiated groups of click types and hinted towards bimodal behavior in some. Click types 4 and 8 were deemed distinct from their respective clusters based on ICI, maintaining potential for a different species label association.

Click type 4 was only found at the coastal, shallow, DCPP site. This click type was 19% of encountered click types at this site, the second largest percentage.

There is a chance that this click type is produced by a distinct species. However, the strong presence of purple group click types at this site and the similarities in spectral and temporal properties to click type 4 means that the same species may be producing both orange and purple group click types. Two click types within the left notch cluster, 2 & 4, display bimodality. The presence of a high point and second point ICI indicate a potential for switching from long range to short range sensing

(Figure 3.7, Nelson & Maclver 2006, Akamatsu et al. 2010, Gassmann et al. 2015).

Click type 8 is recorded less than 1% of the time at DCPP site, but accounts for 7% of encounters at CINMS. This may be a low percentage, but it does maintain consistency with known habitat preferences of Kogia spp. (Willis & Baird 1998,

Staudinger et al. 2014,). The lack of encounter of this click type at GofAK site agrees

53 with current approximated equatorial to mid latitude habitat ranges of Kogia spp.

(NOAA 2010). Other reasons for the lack of click type 8 encounter at the GofAK site are site depth and the short amount of effort spent recoding. The site may be located in too shallow an area for recording deeper associated species. The short amount of effort spent recording at this site resulted in few recordings of NBHF animals and ignores the potential for seasonal presence of a species. Click type 8 is deemed distinct from the right notch cluster by a combination of the position of energy onset, previously discussed, and the peak in ICI, greater than the rest of the right notch cluster. This ICI is 20-30ms longer than reported for the rest of the right notch cluster; click types 5, 6, & 7. Falling within the peak ICI value for NBHF signals from cetaceans in the Gulf of Mexico, ~83ms, where Kogia spp. are presumed to be the sole proprietor. Due to the sample size of click type 8 one can with some uncertainty associate this click type to Kogia spp.

4.4 General Geographic Association

Click types reported for GofAK were few, but were predominantly from the green group (78%), probably produced by Dall’s porpoises. The purple group was also encountered at GofAK in 22% of signals detected, probably produced by harbor porpoises. The green group, probably Dall’s porpoises, was also found in highest quantities at CINMS, the associated Dall’s porpoise and presumed Kogia spp. habitat.

The location of GofAK, beyond 100m water depth, hinders the likelihood of high harbor porpoise presence relating well to the few purple group detections.

Section 5

Conclusion

The results of this study present a novel view for quantifying the spectral and temporal properties of narrow-band high-frequency (NBHF) signals and presents the full variability of these signals produced in the Gulf of Alaska and off the coast of southern California. Compared to previous work, this study employed bottom moored passive acoustics to create associations of NBHF signals to species made using relative dominance at the three study sites.

The NBHF signals reported in this study display blatant and subtle differences for acoustic differentiation. Initially, the most noticeable distinction is determined by the shape of the spectra, energy onset, notch position, and peak frequency. Inter-click interval (ICI) is a more subtle discrimination feature utilized in support of the spectral shape. Harbor porpoise appears to be more readily distinguishable by spectral shape. Left and right notch spectra distinguish speculated harbor porpoise signals from speculated Dall’s and Kogia spp. signals.

The presence of short and long duration ICIs may indicate ability for harbor porpoise to switch between long and short range sensing.

Dall’s and Kogia spp. are more challenging to confidently distinguish. While

ICI may be adequate for distinguishing speculated Dall’s and Kogia spp. signals, the position of energy onset may serve as a more blatant feature in differentiation. More research, of larger sample size, is needed to distinguish Dall’s porpoise and Kogia spp. with confidence. Occurrences of click types correlated to these species labels were not abundant. The low sample size needs to be supported through more bottom moored passive acoustic encounters of NBHF signals in areas where both species have potential for habitat overlap. The recording would ideally be coupled with sightings at the surface confirming species presence and the absence of other

NBHF signal producers in close proximity at the time the encounter was recorded.

Accounting for presence and absence of NBHF signal producers would further corroborate species label association.

Future studies should employ bottom moored passive acoustics at sites where Dall’s porpoise and Kogia spp. are speculated to be present together and isolated from one another. For example, another site in close proximity to CINMS, but at a deeper depth. Possible sites for isolated presence could be in the northern part of the Dall’s porpoise range, Gulf of Alaska, where Kogia spp. are not presumed to be, as well as, in the Gulf of Mexico where Kogia spp. are speculated to be the sole producer of NBHF signals. Close attention to information regarding ICI and position

56 of energy onset should be employed when analyzing future encounters of these species.

The ability to detect these species holds great implications in understanding behavior and monitoring it for changes. In the case of the moderately well monitored and understood species, harbor and Dall’s porpoise, the capabilities of utilizing a bottom moored instrument will further assist in documenting local and regional scale changes in presence, abundance, and density. Whilst in the case of

Kogia spp., researchers can begin to explore the exciting prospect for the potential of distinguishing the two Kogia spp., Kogia breviceps and Kogia sima. The conservation and management for all of these species relies on our ability to understand, monitor, and predict their spatial and temporal patterns. Bottom moored passive acoustics is one method researcher can utilize to conserve resources while answering questions important to species survival in the world’s highly dynamic oceans.

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